Smart City Network Research Articles

Guest Editorial: Recent Advances in Fuzzy-Based Intelligent IoT and Cyber-Physical Systems

The papers in this special section focus on recent advances in fuzzy-based intelligent Internet of Things and cyber-physical systems. The rapid development of real-time Internet of Things (IoT) applications including smart grids, smart city, and intelligent transport networks generate a tremendous amount of data from massively distributed sources, which require high computing and communication demand that frequently exceeds the users’ requirements. Furthermore, many emerging IoT applications including remote surgery, machine monitoring and control, fault detection, and healthcare generate delay-sensitive tasks, which require timely processing with minimum delay. Besides that, cyber-physical systems (CPS) integrate computing and communication capabilities with monitoring and control of entities in the physical world. These systems are usually composed of a set of networked agents, including sensors, actuators, control processing units, and communication devices. All the critical infrastructures are also a part of the cyber-physical ecosystem to enable smart and connected environments.

Cyberattack Detection in IoT- Based Smart City Network Traffic

Cyberattack Detection in IoT- Based Smart City Network Traffic

Governance and human rights implications of ASEAN's Smart Cities Network: a knowledge commons analysis

AbstractLaunched in April 2018, the Association of Southeast Asian Nations' Smart Cities Network (ASCN) initiative raises important issues regarding the tensions between achieving smart city objectives on the one hand and protection of human rights on the other. The aim of this paper is to explore these tensions using a Knowledge Commons Framework analysis. I first analyse the three key pillars of the ASCN pilot city knowledge commons – knowledge resources, community attributes and governance ‘rules in use’ – using human rights criteria. I the apply the lessons of this analysis to two fundamental aspects of human experience in smart city contexts – mobility through transport systems and access to essential services through energy supply.

A near Real-Time Monitoring System Using Public WI-FI Data to Evaluate COVID-19 Social Distance Measures

This study assessed the applicability of geolocation data provided by public Wi-Fi infrastructures as information sources that can contribute to urban planning and management. We focused particularly on modeling and monitoring real-time mobility and congestion using geolocation capabilities of Wi-Fi public networks in Smart cities. The proposed methodology combines a detailed geographic analysis of the space with high-frequency indicators generated from network data. This study emphasizes the importance of Wi-Fi infrastructures as noninvasive monitoring systems, and describes how network data can be applied to generate useful indicators for urban planning and management. The methodology was empirically implemented in the city of Palma (Balearic Islands, Spain), where the social distance level was measured to identify conflicting areas. We demonstrate how the proposed solution can estimate pedestrians’ density efficiently and precisely through high-frequency monitoring (5 min or less) and the construction of comprehensive indicators. In this context, we suggest several public policies that can be implemented by using this methodological approach to monitor dynamic patterns of pedestrian mobility, especially during health crises or during high tourist seasons.

A throughput drop estimation model and its application to joint optimization of transmission power, frequency channel, and channel bonding in IEEE 802.11n WLAN for large-scale IoT environments

The concept of Internet of Things (IoT) has been widely studied in smart home networks, smart city networks, smart grid systems, autonomous driving systems, and smart healthcare systems. In IoT, the IEEE 802.11n wireless local-area network (WLAN) is used as a common communication technology due to its flexibility and low cost. Then, the high performance WLAN is required to enhance quality of service (QoS) of large-scale IoT applications connecting a number of devices or sensors allocated in wide areas. WLAN can use the limited number of partially overlapping channels (POCs) at 2.4 GHz band. The WLAN performance can be degraded by interfered signals from other WLANs. Then, to optimize the POC assignment by reducing interferences, we have proposed the throughput drop estimation model for concurrently communicating multiple links under interferences. Unfortunately, the 40 MHz channel bonding (CB) and the 20 MHz non-CB are considered separately, while the transmission power is always fixed to the maximum. In this paper, we study the throughput drop estimation model under coexistence of CB and non-CB while the transmission power is changed. Then, we present its application to the joint optimization of assigning the transmission power, the frequency channel, and the channel bonding to enhance the throughput performance of IEEE 802.11n WLAN. For evaluations, we compare estimated throughputs by the model with measured ones in various network topologies to verify the model accuracy. Then, we apply the model to the joint assignment optimization in them, and confirm the effectiveness through simulations and experiments using the testbed system.

Green communication approach for the smart city using renewable energy systems

A smart city is an evolving Internet of Things (IoT) technique that links different digital gadgets via a network, offering several new services to the manufacturing and medical field to commerce. A smart city is an omnipresent and fundamental change that has altered the whole environment using Information Communication Technology (ICT) and sensor-enabled IoT gadgets. Renewable energy storage, the solar, wind, and distributed resources can be better integrated into the grid. The leading theory in the digital domain for improved and broad use of all the situations with high digital media accessibility (i.e., video, sound, words, and pictures), nevertheless it is challenging to talk freely about such small appliances because of resource constraints (starving power and battery capacity), and large quantities of the information. The green communication approach for the smart city (GCA-SC) is proposed in this article. Thus, using saved video streams to solve these difficulties is recommended by Hybrid Adaptation and Power Algorithms and Delay-tolerant Streamed Algorithms. A new architecture is similarly proposed for the smart city network. Empirical findings such as power drainage, battery capacity, latency, and bandwidth are acquired and evaluated. It was reached that, with less effort than Baseline, GCA-SC optimises energy drainage, the battery capacity, variance, power delivery ratio of the IoT compatible gadgets in the smart city environment. The simulation analysis of the proposed GCA-SC method enhances the packet delivery ratio of 39% and throughput of 99 kbps. It reduces the delay by 2.5 s and the standard deviation by −0.9 s.

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities

In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of alpha = 1.5 and gamma = 2.5 with the varying number of nodes.

Detection and Blind Channel Estimation for UAV-Aided Wireless Sensor Networks in Smart Cities Under Mobile Jamming Attack

Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit. However, both the uploading and downloading phases can be prone to potential threats and attacks. As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, e.g., it is mounted on a UAV or inside a terrestrial vehicle. In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels in the downloading phase. In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility. To suppress high-power jamming signals, we propose a blind physical-layer technique that jointly detects the UAV and jammer symbols through serial disturbance cancellation based on symbol-level post-sorting of the detector output. Amplitudes, phases, time delays, and Doppler shifts - required to implement the proposed detection strategy - are blindly estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal and the detailed structure of multicarrier modulation format. Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenarios of the jammer.

Blockchain Empowered Smart Home: A Scalable Architecture for Sustainable Smart Cities

Emerging growth in technology has rapidly changed our homes and cities. Present homes and cities will be upgraded to smart homes and smart cities in the near future. Various solutions used to build the smart-city network demand a scalable and decentralized solution. This study proposes a blockchain-empowered decentralized and scalable solution for a sustainable smart-city network. The Internet of Things (IoT), fog nodes, permissioned trust chain, smart contract, blockchain, and InterPlanetary file system (IPFS) are deployed to construct a scalable and decentralized solution for a sustainable smart city. Three main public sector departments, i.e., electricity, water supply, and health care, are studied over the proposed solution. The proposed solution is implemented over constrained application protocol (CoAP) and Ethereum blockchain. The performance of the proposed model is evaluated for 1500 devices and over 10,000 records. A total 77.44% improvement is registered during performance evaluation over a scalable environment. The performance evaluation of each case study and collaborative performance evaluation concludes the improvised performance of the proposed solution for scalable and distributed applications. Better performance, scalability, and the distributed nature of the presented model make it suitable for the sustainable smart-city network.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

Realizing Smart City Infrastructure at Scale, in the Wild: A Case Study

The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.

South–South Cooperation and the Promise of Experimentalist Governance: The ASEAN Smart Cities Network

This article considers the impact that increasing pragmatism and pluralism are having on South–South cooperation (SSC). Focusing on the growing sway of multilateral platforms for cooperation between cities and the reinvigoration of regionalism, it identifies experimentalist design principles for fostering autonomy-enhancing initiatives between developing countries that have the capacity to learn from and scale up locally-informed, adaptive problem solving. The first part of the article frames SSC in light of experimentalist governance theory. The second part provides a case study of the Association of Southeast Asian Nations (ASEAN) Smart Cities Network, an initiative that captures the promise and challenges of enhancing SSC through regional experimentalist governance of city-to-city partnerships.

A cryptographic approach to prevent network incursion for enhancement of QoS in sustainable smart city using MANET

A city-wide communication network capable of supporting a range of applica- tions should be established through the use of wireless sensor networks that collect, analyze, and act on data in order to provide solutions for smart city im- plementation. A smart city network confronts a number of obstacles, including changing the network's coverage area to accommodate different applications, non-uniform node distribution, an increase in message volume, the use of multi- ple communication technologies, and node and message heterogeneity. Numer- ous aspects of smart cities are being investigated, with a few applications being developed in each. This article conducts a survey to describe various sensor network choices for smart cities. The research explores message transmission challenges via the lens of applications such as taxi sharing, ambulance services, and other emergency services that rely on real-time traffic data provided by the smart traffic domain to work successfully. Mobile Ad Hoc Networks (MANETs) are employed in a range of areas due to its advantage in rapidly establishing networks. The network will perform ideally if mobile nodes collaborate and trust one another; hence, message transmission for this service, as well as the utility of MANET routing algorithms, are examined.

Adaptive aggregation based IoT traffic patterns for optimizing smart city network performance

Internet of Things (IoT) technology drives lifestyle changes in smart city infrastructure due to rapid growth of services and activities that develop well-being. One of the main challenges that resulted from the exponential spread of IoT is the dense number of nodes with the huge amount of data over different networks that influence the collision probability, and network congestion. Existing aggregation techniques worked to solve these challenges with overlooking the IoT traffic characteristics and their types. In this paper, adaptive aggregation techniques are proposed based on IoT traffic types to overcome IoT network issues. These techniques can abstract data, reduce number of packets sent with low traffic congestion, and reduce the recurring packet headers. The proposed adaptive aggregation techniques are accomplished over the IoT smart city networks that are architectured and practically tested to examine the simulation results. It is anticipated that the adaptive aggregation results could optimize the operational efficiency of IoT smart city networks in most key performance metrics, compared to the existing aggregation techniques.

After the Epidemic, Is the Smart Traffic Management System a Key Factor in Creating a Green Leisure and Tourism Environment in the Move towards Sustainable Urban Development?

The purpose of this study is to explore, after the epidemic, the intelligent traffic management system, which is the key to creating a green leisure tourism environment in the move towards sustainable urban development. First, quantitative research, snowballing, and convenience sampling methods are used to analyze 750 questionnaires with a basic statistical test, t-test, ANOVA test, and the Pearson product–moment correlation coefficient (PPMCC) method. Qualitative research and a semi-structured interview method are used to collect the opinions of six experts on the data results. Finally, the results are discussed with the multivariate inspection method. Although the current electric bicycle system is convenient, the study found that the service quality of the airport is sufficient; that the fare of the subway is low and popular with students if the system can ease the crowd during peak hours; and that the login and security check time can be shortened, which can help improve the operating convenience of the system interface and link the information of leisure and tourism activities. On the other hand, adjusting fares, increasing seats, planning for women-only ticketing measures and travel space, providing disinfection or cleaning facilities in public areas, and improving passenger’s public health literacy and epidemic prevention cooperation will further enhance the student travel experience, improve the smart city and green tourism network, and help achieve sustainable urban tourism.

Prediction of malicious objects using prey-predator model in Internet of Things (IoT) for smart cities

Presently, the interconnected networks in smart cities are increasing at a high pace using the Internet of Things (IoT), which allows the user to access the device from anywhere and gather the data. With such massive networks, data analysis plays a vital role in the real world to interpret and analyze the information, giving it a meaningful form. Such an extensive network of interconnected devices leads to a high probability of information theft and data alteration/manipulation through malicious attacks, leading to incorrect information floating over the network, resulting in misleading analysis. This may result is significant disaster in FOG computing environment resulting in failure of smart city applications like healthcare, smart traffic and many more. So, to over a model is required to identify the behaviors of attacker/malicious node. Many work are been proposed using clustering, game theory, fuzzy logic and Intrusion Detection System (IDS) to solve this but they do not consider the spread of infection affecting the other nodes. In this work a pre-predator model to determine the malicious node in the network has been proposed. The model consists prey and predator, where prey consists of infected nodes, whereas predators are malicious objects. In IoT networks, the delay is not the real factor in the spread of infection due to the chaotic nature of the malicious objects. So to overcome this issue, delay differential equation modeling is used.The performance of proposed model is compared with fuzzy logic and game theory based existing models taking into time to detect malicious, rate of infection and Count of infected node detected as performance parameters. Results shows 5% and 8% reduction in number of infected nodes as compared to fuzzy logic and game theory-based approach and reduction of 9% in infection rate.

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

Unpacking the '15-Minute City' via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability.

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting.

Cross layer design with weighted sum approach for extending device sustainability in smart cities

IoT associates with MEMS technology and various other technologies to connect smart devices globally. Providing reliable interaction between the smart devices and maintaining the battery sustainability of smart devices are the major demand since most of the IoT devices are run by a battery source. Device sustainability is the prime concern in most the IoT applications such as smart cities, e-health, smart grid, etc. Thus, we have come up with a cross layer design that improves the device sustainability and lifetime of the IoT networks in smart cities. In the proposed design, physical layer and data link layer information is utilized by the network layer to explore the energy efficient communication among the IoT devices, since 70% - 80% of device energy is utilized for its communication. Cross-layer design can be an optimal solution to enhance the modalities of the wireless network. The Weighted sum approach is used in the proposed model which is an effective computation method to analyze and identify the routing metrics that are associated with energy efficiency. Based on the routing metric analysis (Table 2) it is found that a particular combination of routing metrics performs effective power utilization (by attaining optimal transmission power) and achieves sustained device lifetime. From the obtained results, it is noticed that the MTPM + ETP + ND routing metric combination outperforms all the other combinations, and designing the cross-layer mechanism with the weighted sum approach computation gives better network performance without compromising the device lifetime. In smart cities applications, the device scalability is huge, since frequent battery replacement is a very difficult task. Therefore, we come up with an efficient network model that is more suitable to maintain device sustainability in smart cities.

Drone-Edge Coalesce for Energy-Aware and Sustainable Service Delivery for Smart City Applications

In a typical smart city, drones can collect (or sense) massive amount of data, that is sent to a computing capability for further analysis to make useful decision making without human intervention. This data is relayed to the Cloud for processing and analysis due to its large-scale infrastructural capabilities. However, the key goal of the drone deployment in smart city scenarios or urban environments is to provide timely and quick response alongside providing an energy-efficient service delivery. Thus, we need a sustainable solution that can be deployed locally (closer to the data source) in a smart city, to process or analyze the data (generated from smart city sources) and provide timely decision making for smart city applications. Edge computing, popularly known as the “cloud close to the ground”, can provide computational and processing facilities at edge of the network in a smart city. Hence, Edge computing act as an effective alternative solution to process and analyze the data closer to the point of it’s generation. Looking into the above discussion, We propose a novel drone-edge coalesce that provides an energy-aware data processing mechanism for sustainable service delivery in the multi-drone smart city networks. In this model, the edge computing layer is deployed to process and store the data sensed and collected by drones in a smart city. In this context, an adaptive edge node selection mechanism has been designed on the basis of decision tree approach. In this coalesce, we have to deal with the conventional problems related to the collision and congestion while providing low-latency and sustainable data transmission in a smart city. So, We have designed an energy-aware multi-purpose algorithm that avoids collisions and provides a congestion free data transmission. The proposed coalesce is validated in a simulated environment on the basis of several performance metrics such as, throughput, end-to-end delay and energy consumption.