Smart City Services Research Articles

Guest editorial: Artificial intelligence (AI)-driven spectrum management

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

스마트 시티 서비스를 위한 채널 보상 지도 기반 광역 가시 광 통신 기법

The conventional visible light (VL) communication signals based on the LED array and photodiode in the VL channel are severely distorted at the receiving point far away from VL transmitter because of the channel effect of path loss, multipath diffraction, or cochannel interference, and the insufficient compensation of the VL receiver, which results in failure of long-range VL communication for smart city service. In this paper, we consider the wide access VL communication technique that the compensation extent of the channel effect is basically provided in advance by channel compensation map, and proposes the optimal transceiver circuit for wide access VL communication. And so we can see that the proposed wide access VL communication technique overcomes the signal distortion problem through channel compensation by utilizing the optimal threshold voltage of VL receiver, and attains the successful wide access VL communication test in the overall range to 1070 cm distance.

Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city

With the support of 5G system, the hierarchical distributed cloud service network model (HDCSN) is proposed. The model consists of three levels: Access Cloud + Distributed Micro Cloud + Core Cloud, which meets the basic requirements of IMT-2020 (5G) and 3GPP for network system architecture. On the basis of access cloud, the distributed micro-cloud system of Smart City is deployed to migrate the service capabilities of the remote core cloud server to the local area. Users can obtain high-quality low-latency application services from the micro-cloud server in the local area. A resource description model based on resource graph and hierarchical resource vector is established, which enriches the Smart City service mode of IaaS cloud platform and provides a structural basis for optimal scheduling of cluster resources. A multi-node system resource graph scheduling and allocation algorithm VCE-PSO based on particle swarm optimization is proposed to optimize the response speed and resource efficiency of multi-node collaborative scheduling. The example shows that the above key technologies significantly improve 5G. The scheduling optimization and utilization efficiency of various resources in the hierarchical distributed cloud service for the Smart City effectively reduces the response time of the tenant resource request and optimizes the performance of system resource scheduling on the cloud platform.

An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities

The use of the digital twin has been quickly adopted in industry in recent years and continues to gain momentum. The recent redefinition of the digital twin from the digital replica of a physical asset to the replica of a living or nonliving entity has increased its potential. The digital twin not only disrupts industrial processes, but also expands the domain of health and well-being towards fostering smart healthcare services in smart cities. In this paper, we propose an ISO/IEEE 11073 standardized digital twin framework architecture for health and well-being. This framework encompasses the process of data collection from personal health devices, the analysis of this data, and conveying the feedback to the user in a loop cycle. The framework proposes a solution to include not only X73 compliant devices, but also noncompliant health devices, by interfacing them with an X73 wrapper module as we explain in this paper. Besides, we propose a configurable X73 mobile application, designed to work with any X73 compliant device. We designed and implemented the proposed framework, and the X73 mobile app, and conducted an experiment as a proof of concept of the digital twin in the domain of health and well-being in smart cities. The experiment shows promising results and the potential of benefiting from the proposed framework, by gaining insights on the health and well-being of individuals, and providing feedback to the individual and caregiver.

Smart city development projects in the Republic of Korea

Research relevance. The article discusses the experience of South Korea in the sphere of smart city projects. The study's relevance is determined, on the one hand, by the role smart cities play in the creation of the new urban living space, in enhancement of urban economy and, on the other, by the need to analyze and systematize the best practices of smart city development. Research aim. The study describes the development of the 'smart city' concept in South Korea. Smart city is understood as a complex eco-system, which, on the one hand, comprises a range of technological and socio-economic processes and, on the other, involves such actors as government, corporations, research organizations and urban communities. Data and methodology. The study relies on methods of comparative and evolutionary analysis to analyze the concept of smart city and its structure by focusing on the case of South Korea, the world's renowned ICT leader. The study also provides cross-national comparisons of the experience of other developed Asian countries (Japan and China), which, like South Korea, are actively supporting building of smart cities on the state level. Results. The study describes the main stages in the development of the smart city concept in South Korea as well as the main types of smart city projects (pilot projects, research validation projects, and city restoration projects). The study identifies problems of planning, design and implementation of smart city projects and compares the relevant experience of South Korea, Japan and China. It is shown that although smart city projects in these countries were launched in approximately the same period, each of these countries has its own approach to smart cities and ways of their development. Conclusions. The key to Korea's success in smart city development lies in large-scale direct state support, public trust in the government and the application of an integrated approach to smart city development. At the current stage, smart city services not only provide enhanced comfort to city dwellers but also create more opportunities for their personal growth.

A cost-effective viable strategy for gradually transitioning Egypt’s cities into truly IOT-enabled smart cities

A smart city is a city that uses information and communication technologies (ICT), smart sensors, actuators and video cameras to better support and optimize the delivery of urban services and to address challenges within city communities. A smart city collects and analyzes data from smart IoT sensors (IoT devices/nodes) and video cameras. The data collected by the sensor nodes can be used to develop applications and systems that benefit the city and the community. Smart city industry is projected to be a $400 billion market by 2021, with 600 cities around the globe expected to generate 60% of the world's GDP by 2025, according to McKinsey research. Many cities have adopted at least one smart city project. However, the majority of cities around the globe are not yet smart cities. The key hurdle is securing the huge fund needed to start a smart city project, and ensuring that there are sufficient resources to sustain the project over time. Because implementing new smart city infrastructure is a large and complex financial undertaking that poses a financial hurdle, specifically, for most of the under-developed countries, a viable and cost-effective strategy is needed to tackle this problem and conquer the digital divide. Building upon and leveraging existing infrastructure is the key to address this problem. This minimizes the cost of technology upgrades and allows for an easier integration process. In addition, leveraging existing infrastructure can give cities a better and quicker return on investment. The main objective of this work is to devise an innovative research and development initiative, which builds upon and leverages ongoing global deployment of smart city and outdoor lighting infrastructures trend, to explore the potential and assess the feasibility of gradually transitioning existing Egypt’s cities infrastructure into truly IoT-enabled smart cities. Specifically, we propose and develop an innovative cost-effective, future-proof, scalable and modular end-to-end device-to-cloud connected outdoor Lighting network infrastructure solution that is based on P2P 4G LTE cellular technology to provide the direct connectivity between lighting poles (IoT devices) and the cloud. The proposed intelligent Lighting solution includes three key building blocks:1) light pole-mounted smart control nodes; 2) cloud-based smart city software solution (NetServ) to remotely monitor, manage, and control the entire lighting infrastructure; and 3) P2P 4G LTE cellular network. In the proposed architecture, smart streetlights serve as the framework for a high- bandwidth, low-latency wireless sensor network, capable of transporting large amounts of data in real-time, while concurrently supporting deployment of a wide range of smart city services.

BIoTope: Building an IoT Open Innovation Ecosystem for Smart Cities

The Internet of Things (IoT) has led towards a digital world in which everything becomes connected. Unfortunately, most of the currently marketed connected devices feed vertically-oriented closed systems (commonly referred to as vertical silos) which prevent the development of a unified global IoT. This issue is all the more valid in complex environments, such as smart cities, in which exceedingly large amounts of heterogeneous sensor data are collected, and in which platforms and stakeholders should also be able to interact and cooperate. Therefore, it is of utmost importance to move towards the creation of open IoT ecosystems to support efficient smart city service integration, discovery and composition. This paper contributes to the specifications of such an ecosystem, which has been developed as part of the EU's H2020 bIoTope project. The novelty of this ecosystem compared with the current literature is threefold: (i) it is based on the extensive use of open communication and data standards, notably O-MI and O-DF standards, that foster technical, syntactic and semantic interoperability over domains; (ii) it proposes an innovative service marketplace for data/service publication, discovery and incentivization; (iii) it integrates security functionalities at the IoT gateway level. The practicability of our ecosystem has been validated through several smart city proofs-of-concept set up in three distinct cities: Helsinki, Lyon and Brussels. Given the five major themes defined in the CITYKeys (a smart city performance indicator framework), namely People, Planet, Prosperity, Governance and Propagation, bIoTope mainly contributes to Prosperity-related metrics, as discussed in this paper.

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area.

On-board unit to connect personal mobility vehicles to the IoT

Abstract The basis of smart city services is urban sensing, i.e., the collection of data from sensors deployed in urban settings. However, it has been recently when mobile objects have started to be monitored to feed smart applications within the smart city ecosystems. Most of the advances in this area focus on gathering data from public transportation services such as buses or trams, which use to employ regular cellular or WiFi-based networks and state-of-the-art TCP/IP protocols. Although this covers a basic on-board equipment to report position and other sensor data, there is not a direct integration of Internet of Things (IoT) capabilities in urban transportation and, in general, in moving “things”. Communication technologies used in regular wireless sensor networks such as ZigBee or Bluetooth are not appropriate for high mobility patterns. The Internet of Vehicles (IoV) paradigm aims at filling these gaps, exploiting the synergy between IoT and vehicular networks. It is an emerging area in which extra efforts should be paid to cover constrained capabilities of end devices installed on personal mobility devices, e.g., scooters, segways or bicycles. The solution proposed here deals with the connectivity of moving objects by developing an on-board unit for energy-constrained vehicles, integrating communication capabilities using the Low-Power Wide Area Network (LPWAN) technologies Long Range Wide-area Network (LoRaWAN) and Narrow-Band IoT (NB-IoT) to efficiently collect data. A set of sensors are included to target services in the areas of urban mobility, enhanced environmental perception and speed-up of city operations. The on-board unit has been evaluated through real-life experiments to study especially its communication capabilities with NB-IoT, obtaining good performances in terms of packet delivery ratio and network delay under non-ideal coverage settings. Results show that NB-IoT presents better performances than LoRaWAN, as compared to our previous experiences, with negligible packet loss rates and two-way delays under 100 ms with Internet nodes, at the expense of needing a proper operator network deployment and consuming extra energy due to NB-IoT protocol complexity.

СТАНДАРТИ ТА ТЕХНОЛОГІЙ МЕРЕЖІ 5GІ МОЖЛИВІСТЬ ЇЇ РЕАЛІЗАЦІЇ

The paper investigates the architecture of the 5G network and proposed a variant of the SCOM based parameter monitoring system. You can use this system to collect and analyze network performance information, detect deviations, and notify them for timely removal. Technologies were introduced for the use of the fifth generation GPP mobile network of the fifth generation. Analyzed and proposed for implementation. The current capabilities of the 5th generation network and the technologies for their implementation were also explored. This paper analyzes and provides recommendations for the implementation of the following servers. 5G networks make it possible to significantly increase data rates through various radio access technologies (RATs), and through the use of new 5G NR (New Radio) radio spectrum. Smart Home and Smart Building are available in a variety of different Internet of Things (IoT) services: video surveillance, home automation and control, security management, and more. Virtual Reality (VR) service creates the illusion of moving a person to another world, affecting the sense organs, especially the sight (VR-glasses). Augmented Reality (Augmented Reality) Augmented Reality service combines a real environment with virtual objects. These services are intended not only for entertainment but also for science. The 5G network, along with IoT Internet of Things technology, with the help of Industrial IIoT (Industrial Internet of Things) sensors, as well as AI (Artificial Intelligence), can significantly increase the degree of automation of production. This gives the opportunity in real time to analyze large amounts of diverse data (Big Data), both on the basis of insights, and using machine and deep learning (Machine learning, Deep learning). These may include, for example, e-Health, Mission Critical Communication, Tactile Internet, and others. Unmanned transport may be used as part of the Smart City service, but may exist separately. Also on the 5G platform it is possible to implement ADAS (Advanced Driver-Assistance Systems) driver assistance systems.

Replay Attack Detection in Smart Cities Using Deep Learning

Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research.

E-Government Insights to Smart Cities Research: European Union (EU) Study and the Role of Regulations

The recent evolution of smart cities research has initiated a holistic dialogue for the integration of past initiatives promoting e-government at European Union Level. At the same moment Future Smart Cities research, is justified as a multi-disciplinary and inter-disciplinary research domain. Within this context this research work provides insights for the integration of Economics, E-government, Information Systems, and Social Sciences. The study addresses the e-government process as one of the most important application of smart cities in our current societies. More specifically, we have examined the existence of $\beta $ -convergence between EU member states in terms of e-government services, confirming the hypothesis that low performance countries record higher growth rate than developed countries. In order to move closer to the factors with high impact on e-government development, we have analyzed other variables for testing the differences between countries, in terms of education, digital skills and access to internet for controlling the hypothesis that countries initially have different development conditions and they will not probably converge to the same steady state. We found that the opportunity of using e-services, particularly e-government services, is less accessible to citizens with low overall digital skills and, as is technically normal, to those with low access to internet access. In this context, at EU level it is needed to rethink and design the e-government services in order to be adapted not only to needs of the citizens, but also to their digital skills. The main contribution of this research is two-fold: From one side provides an integrated study with emphasis on the impact of social sciences and economics research to Future Smart Cities Research and from the other side it brings forward several soft factors for the adoption of Smart Cities services in the context of government transformation and provision of ubiquitous e-services to citizens.

Hacking the hackathon format to empower citizens in outsmarting “smart” cities

This paper investigates the opportunities of leveraging a hackathon format to empower citizens’ abilities of using open data to improve their neighbourhoods and communities. The presented discussion is grounded in five civic hackathon case studies organised in five european cities. The research revealed specialised learning and collaborative alignment as two mutually complementing aspects of the involved learning processes, which were achieved with the help of high-fidelity and low-fidelity prototypes, respectively. Consequently, the paper identifies and discusses three main factors required to sustain social learning ecosystems beyond hackathon events, and with the purpose of democratisation of smart city services. These factors include a) supporting individuals in obtaining specific expert knowledge and skills, b) nurturing data-literate activist communities of practice made up of citizens with complementary expert skillsets, and c) enabling members of these communities to generate varying fidelity prototypes of open-data services.

On Transparency and Accountability of Smart Assistants in Smart Cities

Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

Customer Perception and Problems towards Ola Services in Smart Cities with Reference to Salem

Customer Perception and Problems towards Ola Services in Smart Cities with Reference to Salem

Factors that determine residents’ acceptance of smart city technologies

ABSTRACTWhile some cities attempt to determine their residents’ demand for smart-city technologies, others simply move forward with smart-related strategies and projects. This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services. A Smart Cities Stakeholders Adoption Model (SSA), based on Unified Theory of Acceptance and Use of Technology (UTAUT2), is developed and tested on a mid-size U.S. city as a case study. A questionnaire was administered in order to determine the influence of seven factors – effort expectancy, self-efficacy, perceived privacy, perceived security, trust in technology, price value and trust in government – on behaviour intention, specifically the decision to adopt smart-city technologies. Results show that each of these factors significantly influenced citizen intention to use smart-city services. They also reveal perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of trust in government. In turn, both types of trust are shown to increase user intention to both adopt and use smart-city services. These findings offer city officials an approach to gauging residential intention to use smart-city services, as well as identify those factors critical to developing a successful smart-city strategy.

Information management in IoT cloud-based tele-rehabilitation as a service for smart cities: Comparison of NoSQL approaches

Nowadays, recent advancements in ICT have sped up the development of new services for smart cities in different application domains. One of these is definitely healthcare. In this context, remote patient monitoring and rehabilitation activities can take place either in satellite hospital centres or directly in citizens’ homes. Specifically, using a combination of Cloud computing, Internet of Things (IoT) and big data analytics technologies, patients with motor disabilities can be remotely assisted avoiding stressful waiting times and overcoming geographical barriers. This paper focuses on the Tele-Rehabilitation as a Service (TRaaS) concept. Such a service generates healthcare big data coming from remote rehabilitation devices used by patients that need to be processed in the hospital Cloud. Specifically, after a feasibility analysis, by using a Lokomat dataset as sample, we measured and compared the performances of four of the major NoSQL DBMS(s) demonstrating that the document approach well suits our case study.

Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers

Abstract Solar energy is becoming one of the most attractive renewable sources. In many cases, due to a wide range of financial or installation limitations, off-grid small scale micro power panels are favoured as modular systems to power lighting in gardens or to be integrated together to power small devices such as mobile phone chargers and distributed smart city facilities and services. Manufacturers and systems’ integrators have a wide range of options of micro-scale photo voltaic panels to choose from. This makes the selection of the right panel a challenging task and risky investment. To address this and to help manufacturers, this paper suggests and evaluates a novel approach based on integrating empirical lab-testing with short-term real data and neural networks to assess the performance of micro-scale photovoltaic panels and their suitability for a specific application in specific environment. The paper outlines the combination of lab testing power output under seasonal and hourly conditions during the year combined with environmental and operating conditions such as temperature, dust accumulation and tilt angle performance. Based on the lab results, a short in-situ experimental work is implemented and the performance over the year in the selected location in Kuwait is evaluated using deep learning neural networks. The findings of this approach are compared with simulation and long-term real data. The results show a maximum error of 23% of the neural network output when compared with the actual data, and a correlation values with previous work within 87.3% and 91.9% which indicate that the proposed approach could provide an experimental rapid and accurate assessment of the expected power output. Hence, supporting the rapid decision-making process for manufacturers and reducing investment risks.

RAMTEL: Robust Acoustic Motion Tracking Using Extreme Learning Machine for Smart Cities

Motion tracking is attractive in what concerns a smart city environment, where citizens have to interact with Internet of Things (IoT) infrastructures spread all around one particular city. Motion tracking is important for smart services and location-based services in smart cities, since it provides natural ways for users to interact with the IoT infrastructures, such as the ability to recognize of a wide range of hand motion in real-time. Compared with dedicated hardware devices, ubiquitous devices with reliable speakers and microphones can be developed to achieve cheap acoustic-based motion tracking, which is appropriate for low-power and low-cost IoT applications. However, for complex urban environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter called multipath dispersion vector (MDV) is proposed to estimate and mitigate the impact of multipath fading on received signals using extreme learning machine. Based on MDV, a robust acoustic motion tracking (RAMTEL) method is proposed to calculate the moving distance based on the phase change of acoustic signals, and track the corresponding motion in 2-D plane by using multiple speakers. The method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, RAMTEL can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2 and 4 mm in 1-D and 2-D scenarios, respectively.

Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-$k$ Spatial–Textual Query

The convergence of technologies like Cloud computing, mobile, and smart phone technologies has led to the rapid development of location-based services (LBS) in smart cities. For flexibility and cost savings, there is a recent trend to migrate LBS to the Cloud, however it poses a serious threat to the user privacy. In this paper, we present a new privacy preserving top- $k$ spatio-textual keyword ( $\text{T}{k}$ SK) query scheme, called privacy-preserving spatio-textual index (Pystin), which is performed over outsourced Cloud and can enable secure LBS in smart cities. In Pystin, a query user’s accurate location is protected by the combination of Boneh–Goh–Nissim homomorphic encryption and hash bucket techniques, and the privacy of textual information are persevered by a one-way hash function. In addition, a quad-tree-based spatio-textual indexing is integrated into Pystin to further reduce the query latency. Detailed security analyzes show that the proposed Pystin scheme is indeed a privacy-preserving $\text{T}{k}$ SK query scheme. Furthermore, extensive experiments are conducted, and results confirm the scalability, efficiency properties of our proposed Pystin scheme.