Smart City Services Research Articles

A Framework for Simulating the Suitability of Data Usage in Designing Smart City Services

A Framework for Simulating the Suitability of Data Usage in Designing Smart City Services

Pseudolites to Support Location Services in Smart Cities: Review and Prospects

The location service is an important part of the smart city. A unified location service for outdoor and indoor/overground and underground activity will assist the construction of smart cities. However, with different coordinate systems and data formats, it is difficult to unify various positioning technologies on the same basis. Global navigation satellite system (GNSS)-based positioning is the only way to provide absolute location under the Earth-centered, Earth-fixed coordinate system (ECEF). Increasing indoor and underground human activity places significant demand on location-based services but no GNSS signals are available there. Fortunately, a type of satellite that is indoors, known as pseudolite, can transmit GNSS-like ranging signals. Users can obtain their position by receiving ranging signals and their resection without adding or switching other sensors when they go from outdoors to indoors. To complete the outreach of the GNSS indoors and underground to support the smart city, how to adapt the pseudolite design and unify coordinate frames for linking to the GNSS remain to be determined. In this regard, we provide an overview of the history of the research and application of pseudolites, the research progress from both the system side and the user side, and the plans for pseudolite-based location services in smart cities.

Joint Traffic Offloading and Aging Control in 5G IoT Networks

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.

Citizen-centric Design of Consumable Services for Smart Cities

The ongoing growth of city populations has brought new challenges and increased complexity to everyday city life. In response, numerous cities are making efforts to adopt new information technologies to become more efficient and transform themselves into smart cities. A large body of research has underlined that the success of these smart city initiatives depends on putting citizens at the center of the design process. In contrast to the techno-centric view applied in the past, citizen-centric approaches focus on prioritizing the needs of citizens over technology and aim at involving them directly in the development of smart city services. In this article, we describe this involvement as citizen development by introducing a model-based architecture that enables citizens to develop smart city services in a low-code fashion. Consequently, we focus on citizen developers, a group of citizens engaged with the technological development of services. Furthermore, the conceptualization of a citizen development architecture is discussed, as well as the ways citizens can interact with it. An important pillar in this approach are citizen workshops, which aim at stimulating innovative ideas and collaboration among stakeholders by using design thinking methodology. Our approach empowers citizens to become active producers within the smart city ecosystem, leading to more inclusive and citizen-centric smart cities.

A Queueing Model-Based Experimental Analysis of Mobile-Energy Distribution Stations (M-EDS) for Smart City Urbanization

Smart devices, terminals, energy grids, houses, users, and companies united under one roof have recently grown into smart cities using various technical tools and ways to communicate, process, and exchange information. Urbanization plays a significant part in developing smart cities among the many application services that smart cities offer. Many users/consumers who live in rural areas commute daily to urban areas for jobs, school, and other purposes. There are not many people there, therefore building comprehensive smart city services would be a waste of time, money, and resources. However, there is a chance that urbanisation may create a small-scale industry for development and a beneficial energy grid for those living in rural areas. A completely functional energy grid is also challenging to build; one must comprehend and determine the parameters before manufacturing. To examine the incoming energy rate from rural areas connected to the primary smart city energy grid, this article presents an efficient Mobile Energy Distribution Substation (M-EDS). Every home's energy inflow rate is assessed, and resources are distributed following the queueing criteria the M-EDS has examined. Two categories—dynamic energy and fixed energy—are used to measure the rate of incoming energy. The suggested mobile energy distribution substation's performance is examined considering these two evaluations, and its benefits and drawbacks are highlighted.

Assessment of Dynamic Object Information Utilization Service in a Control Center for Each Urban Scale via Fuzzy AHP

Recently, the demand for citizen-sensible service solutions such as traffic, crime prevention, and disasters in smart cities is increasing. In order to provide technology-based smart city services, local government control centers could be utilized. Accordingly, this paper presented a method for selecting a control center-based dynamic object information utilization service model through in-depth interviews with 26 related local government control center operation personnel. A comparative analysis according to the size of the local government to which the evaluator belongs was also performed. As a methodology, Fuzzy AHP was used, which can support rational decision-making by mathematically expressing ambiguous phenomena such as subjective and uncertain judgments. The summary of the research results is as follows. Services related to recent incidents in South Korea (e.g., school zone traffic accidents and lowland inundation) were identified as very important. These social issues are significant factors in policy decisions. In comparing the results for each urban scale, the importance of pedestrian safety services on backside roads and main road traffic services was found to be important in the metropolitan area and regional, medium, and small cities, respectively. This was attributed to metropolitan cities with high population density, and medium and small cities experiencing alienated traffic information. In metropolitan areas, new services are highly important owing to the demand for a more scientific control service and future mobility based on a sound control infrastructure. In medium and small cities, facility management services were assessed relatively highly owing to the poor conditions of regional cities with a lack of supervising personnel in the field and a lack of surveillance system infrastructure. This paper was able to confirm the difference in service preference by city size, and it is necessary to select the optimal service model considering these results.

A Community-Oriented Accessibility Index of Public Health Service Facilities: A Case Study of Wuchang District, Wuhan, China

Public health service facilities are the fundamental component of urban medical and health services, and they are critical for realizing urban residents’ enjoyment of equitable and convenient medical services in sustainable smart cities. Spatial accessibility is an important indicator for evaluating the rationality of the layout of medical facilities from the perspective of sustainability. This study introduces a community-oriented accessibility (CA) index of public health service facilities, which considers a weighted average time model and the 15 min living circle standard for constraint, and even the service capacity of medical facilities. This study uses the proposed index to investigate the spatial accessibility of public health service facilities in the study area of Wuchang, Wuhan, China. Compared with the E2SFCA model, the proposed CA model joins the construction concept of an urban living circle, which is consistent with the domestic urban construction goals. The main findings include the following: (1) The community-oriented accessibility index of public health service facilities in the study area showed a gradual decline in the middle to sides under the walking mode and a high index in the north and low index in the south under the vehicle travel mode. (2) The calculated CA accessibility results are quite different between the walking and vehicular modes, and they are mainly affected by the allocation of the number of facilities and the distribution of the population. (3) Compared to the previous method, the calculated CA index results are more reflective of the actual situation and could be useful in guiding the spatial layout of the facilities more finely. This research explores the sustainable utilization of land resources in the planning of medical facilities and promotes the healthy and sustainable development of cities. In future studies, the population’s actual traffic conditions, weather, and holidays will be considered to further research on the accessibility of different groups and the specific impact of these factors on accessibility so as to promote people-friendly accessibility for sustainable smart cities.

Decision-Making Support System for Quality Smart City Services

This paper presents the results of research carried out in the field of developing decision support systems for quality Smart City services. The decision-making system consists of two sets of Agents: Service Provider Agents and Service Consumer Agents. The interaction between the Agent sets is governed by the knowledge base which is managed by the Service Quality Assessors. Service quality is evaluated based on a Multi-Objective Optimization model competitively performed by applying game theory (Nash Equilibrium) between Agent sets involving available resources and knowledge. The paper developed: interaction diagram between Agents and services offered by Smart City, interaction diagram between sets of Agents to provide quality services, and multi-level infrastructure diagram for decision support system. The Multi-Objective Optimization problem is defined in the form of the set of objective functions for the evaluation of service quality and the set of constraint functions for service state parameters and action parameters for service provision.

A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services

As the number of devices connected to the Internet has grown larger, so too has the intensity of the tasks that these devices need to perform. Modern networks are more frequently working to perform computationally intensive tasks on low-power devices and low-end hardware. Current architectures and platforms tend towards centralized and resource-rich cloud computing approaches to address these deficits. However, edge computing presents a much more viable and flexible alternative. Edge computing refers to a distributed and decentralized network architecture in which demanding tasks such as image recognition, smart city services, and high-intensity data processing tasks can be distributed over a number of integrated network devices. In this article, we provide a comprehensive survey for emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. We start by analyzing the rise of cloud computing, discuss its weak points, and identify situations in which edge computing provides advantages over traditional cloud computing architectures. We then divulge details of the survey: the first section identifies opportunities and domains for edge computing growth, the second identifies algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzes situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follows. The primary discourse of this article is in service of an effort to ensure that appropriate approaches are applied adequately to artificial intelligence implementations in edge systems, mainly, the lightweight machine learning approaches.

Identifying and Evaluating Factors Affecting User Privacy in the Smart City Using the Meta-Synthesis Method and the Fuzzy Dematel Technique

Smart cities are becoming increasingly popular today due to various digital technologies in their infrastructure. One of the most critical challenges in the proper implementation and establishment of these cities is the violation of the security and privacy of users, which leads to citizens’ distrust and pessimism about smart city services. Many factors can affect the protection of users’ privacy. Identifying these factors helps developers understand what dimensions and aspects affect users’ privacy, through which they can provide appropriate solutions to protect the privacy of the user. Therefore, this study identifies and evaluates these dimensions. The authors proposed a novel methodology for identifying factors influencing user privacy in smart cities using the meta-synthesis method and the fuzzy decision-making trial and evaluation laboratory (DEMATEL). As a result of the meta-synthesis application, the authors identified seven dimensions of interest, specifically, Awareness and understanding, Trust, Self-control and personalization, Transparency, Reference standards frameworks and rules, Data management, and Security requirements. Moreover, using the DEMATEL technique and expert-based reviews, those seven dimensions were classified as cause and effect. As a result, the authors discovered that security requirements (D7) are the only cause dimension. As effect dimensions, “awareness and understanding (D1),” “data management (D6),” “trust (D2),” “frameworks and standards (D5),” “personalization and self-control (D3),” and “transparency (D4)” have been identified. Furthermore, the authors discovered that transparency is the most effective dimension in terms of affecting user privacy in smart cities. These findings may serve as guidelines for implementing the smart cities initiatives and protecting users’ privacy. This study also provides a discussion and insights for smart city developers.

Dynamic Multiworkflow Offloading and Scheduling Under Soft Deadlines in the Cloud-Edge Environment

Computing offloading and scheduling are emerging, accompanied by smart city services, Internet of Things, and other rich cloud services. Cloud-assisted mobile edge computing provides malleable resources but introduces considerable overhead. Due to the geographical distributions and distances of the edge, cloud, and mobile terminal, promoting cost efficiency and reducing the makespan of applications are urgent targets. For multiple mobile applications, the soft deadline constraint for each application is common in the edge-cloud environment. When multiple users initiate application requests, scheduling on the target offloaded platform commences. Some workflows, especially newly arrived workflows, might suffer from “starving to death,” which has the risk of being missed. This article describes a dynamic, multiworkflow offloading, and scheduling, adaptive heterogeneous earliest-finish-time-based algorithm, which can lightly run on a distributed network. We explore an innovative model composed of a multiobjective makespan and cost for a soft deadline constraint optimization problem. Abundant data are adopted to test the offloading and scheduling effect on a variety of platform parameters, and comparative experiments demonstrate the validity and efficiency of the adaptive system.

Editorial Security, Reliability, and Resilience in Internet of Things

The Internet of Things (IoT) has become a popular platform that supports various services, such as smart homes, industrial applications, microsurveillance systems, and smart city services. IoT connects physical devices, such as sensors, thermostats, cell phones, laptops, and servers, and enables them to collect and exchange data. It has been predicted that the number of IoT devices will reach billions by the year of 2025. These sensing devices often generate large amounts of sensitive data and communicate frequently through open channels at a high risk of being compromised. Due to the mission-critical nature of IoT applications, IoT devices must operate in a secure and reliable manner. Any network outage, data loss, or corruption during transmission or in storage can have catastrophic effects on the IoT applications. For their deployment, security and reliability assurance are essential. IoT devices may span multiple layers of the IoT architecture, (e.g., cloud, edge, and sensors) and vary in various service modes, critical levels, and connectivity. When an IoT device encounters unexpected events, it needs to automatically reconfigure its settings and operations, and sometimes even suspend its transmission in a way that cyber resiliency can be achieved.

An energy efficient fog level resource management scheme for software defined cities

The proliferation of Internet of Things (IoT) and smart city services have paved the path for complex network architectures including edge and fog layer resources in order to meet such services’ Quality of Service (QoS). Such services should also be energy efficient for sustainable cities. Though energy efficiency has been widely studied over the past decade, yet most such solutions have focused on device or sensor level solutions or alternatively on cloud/datacenter level solutions. With the prominence of Software Defined Networks (SDNs) in recent times owing to its flexible and scalable network management features, SDN based fog level resource management has become an attractive alternative. This paper addresses fog level energy-aware resource management with the goal of minimizing energy consumption while QoS fulfillment by means of Virtual Machine (VM) consolidation through live migrations. Since any decent sized smart city would have millions of devices and large numbers of edge/fog instances, it would lead any solution mechanism to a NP hard problem. This paper proposes a novel set based Differential Evolutionary (S-DE) algorithm. Simulations have been carried out to test the efficacy of the proposed S-DE solution and the results demonstrate the efficiency of the proposed method.

A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services

Energy efficient information and communication technology (ICT) infrastructure at all levels of a city’s edifice constitutes a core requirement within the sustainable development goals. The ICT infrastructure of smart cities can be considered in three levels, namely the cloud layer infrastructure, devices/sensing layer infrastructure, and fog layer infrastructure at the edge of the network. Efficiency of a data-centre’s energy infrastructure is significantly affected by the placement of virtual machines (VMs) within the data-centre facility. This research establishes the virtual machine (VM) placement problem as an optimisation problem, and due to its adaptability for such complicated search issues, this paper applies the genetic algorithm (GA) towards the VM placement problem solution. When allocating or reallocating a VM, there is a large quantity of unused resources that might be used, however these resources are inefficiently spread over several different active physical machines (PMs). This study aims to increase the data-centre’s efficiency in terms of both energy usage and time spent on maintenance, and introduces a novel fitness function to streamline the process of computing the fitness function in GAs, which is the most computationally intensive component in a GA. A standard GA and first fit decreasing GA (FFD-GA) are applied on benchmark datasets to compare their relative performances. Experimental results obtained using data from Google data-centres demonstrate that the proposed FFD-GA saves around 8% more energy than a standard GA while reducing the computational overhead by approximately 66%.

Real-World Analysis of Smart City Integrated Services using High-Speed Network Technologies

Abstract: Services for smart cities are required to improve productivity, sustainability, and quality of life. They can maximise resource usage, enhance public safety, accelerate economic growth, and promote civic engagement. In the past, cities relied on unreliable and limited-range radio and wired telephone networks. The capabilities of networked city services have significantly increased with the development of technologies like Ethernet and passive optical network (PON). These technologies enable real-time data collecting and analysis by delivering higher data transfer speeds and greater coverage areas. PON connections offer higher bandwidth, greater reliability, and longer lifetimes; 50 Gigabit-PON has the capacity to support different commercial services with notable performance to meet service requirements; and Ethernet networks have lower deployment costs as well as greater flexibility in terms of adding or removing devices. The various use-case scenarios of high-speed network technologies are evaluated. The technology ultimately chosen will be determined by the specific requirements and objectives of the smart city project.

Innovations in Shared Mobility—Review of Scientific Works

Shared mobility is developing at a very fast pace around the world, becoming an alternative to classic forms of travel and, according to the public, providing innovative services. In recent years, these innovative services have also gained wide interest among scientists from a multicriteria point of view. However, among the topics and reviews in the literature, no review paper considering shared mobility in terms of innovation was identified. This article’s research objective was to indicate the perception of innovation in shared mobility in scientific works. The results indicate that innovations in shared mobility are a niche topic considered in few scientific works. What is more, in most cases, shared mobility services are perceived as innovative in themselves without detailed service analysis. Moreover, the issues of open innovation, which are closely related to the concept of accessible Mobility as a Service system and smart cities, are often overlooked. In addition, there was no work identified that fully referred to all areas of innovative service. The article supports researchers in the determination of further research directions in the field of shared mobility and fills the research gap in the field of knowledge about open innovation, especially in the context of the development of shared mobility services in smart cities.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

Studi Komparasi Layanan Smart City Antara Kota Semarang dan Kota Malang


 Aulia Surya Pertiwi1, Ilmi Sulistiati Anisa2, Muhammad Thoriq Ramadhan3, Siti Wahyuni Rahmawati4, Tria Rama Nabila5, Irvan Arif Kurniawan6
 Universitas Islam Syekh-Yusuf Tangerang, Fakultas Ilmu Sosial dan politik. 
 Program studi Ilmu Administrasi Negara
 auliasryprtwi@gmail.com1, ilmysulistiatianisa@gmail.com2, rthoriq23@gmail.com3, yunnir016@gmail.com4, trianabila112@gmail.com5, iakurniawan@unis.ac.id6
 
 ABSTRACT.
 A smart city is one that includes contemporary infrastructure and transportation systems, Additionally to human and social capital, create high-quality, long-term economic growth. The idea of the "Smart City" is becoming prevalent in major cities around the globe. This also applies to Indonesia. 2020 saw the release of the "Movement Towards 100 Smart Cities" by the Indonesian government. This was a significant milestone in the country's effort to digitize public services. Semarang City is one of the cities used as a model for the growth of smart cities in Indonesia. While it cannot be separated from that, one of the top aims for Malang Metropolis's development in 2022 is to realize a Smart City-based city. The chosen method is to use a literature review as a research strategy. In this literature study research, which is a series of events or activities related to library data collection methods, specifically by recording and processing research material taken from previous studies, the implementation of Smart City services in the cities of Semarang and Malang is being compared. In order to maximize the efficiency of creating a city, enhance the quality of life, and boost the local economy, the implementation of smart cities in the cities of Semarang and Malang has utilized and applied a universal method. Based on research supporting Cohen Boyd's "Smart City" theory, this is said.
 Keywords: Smart City, City Development, Semarang City, Malang City

Evaluation and Application of Public Management Service Quality of Smart City Based on Intelligent Computing

The service quality of smart city public management is the direction of modern city management construction of Chinese local government, but the current work of smart city management construction and management is not perfect. Among them, the most obvious and prominent problem is that there is no evaluation system for the public management service quality of smart cities, which makes it difficult to achieve the expected effect in the actual construction process without considering all factors. In this case, it is necessary to establish a complete and effective smart city management evaluation system to ensure the rationality and effectiveness of urban management input, which is a problem that must be solved by the current smart city local government smart city public management service quality. In this context, this paper first introduces and analyzes the research background, purpose, and significance of public satisfaction with public information services in smart cities. The origin, significance, and application status of smart cities are sorted out, the relationship between them is elaborated, and the overall research framework is determined. Second, the evaluation model of public management service quality of a smart city based on the fuzzy analytic hierarchy process is constructed, and the theoretical basis of this satisfaction model is elaborated. Finally, the simulation results demonstrate the effectiveness and practicability of the proposed method.

Data driven approaches for smart city planning and design: a case scenario on urban data management

PurposeBecause of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development.Design/methodology/approachDesign science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project.FindingsThe findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design.Research limitations/implicationsFeedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design.Practical implicationsFindings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development.Social implicationsThe main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design.Originality/valueThis study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.