Role In Smart Cities Research Articles

Role of Internet of Things (IoT) and Crowdsourcing in Smart City Projects

This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban development. Based on an extensive literature review, the paper first presents the smart city concept, emphasizing smart city architecture and the role of data in smart city solutions. The second part presents the Internet of Things, focusing on IoT technology, the use of IoT in smart city applications, and security. Finally, the paper presents crowdsourcing with particular attention to mobile crowdsourcing and its role in smart cities. The paper shows that IoT and crowdsourcing have a crucial role in two fundamental layers of smart city applications, namely, the data collection and services layers. Since these two layers ensure the connection between the physical and digital worlds, they constitute the central pillars of smart city projects. The literature review also shows that the smart city development still requires stronger cooperation between the smart city technology-centered research, mainly based on the IoT, and the smart city citizens-centered research, mainly based on crowdsourcing. This cooperation could beneficiate in recent developments in the field of crowdsensing that combines IoT and crowdsourcing.

Iot based intelligent route selection of wastage segregation for smart cities using solar energy

The waste management is a major problem all over the world. The smart is an innovative automated device for the separate collection of used absorbent hygiene products. The Automatic Intelligence Smart Bin helps to solve waste management problem in smart cities. The smart bin will play a important role in smart cities. The smart lifestyle begins with cleanliness environment within the city which originates with smart bin. The smart bin utilized HC-SR04 Ultrasonic Sensor is used to identify human being and TowerPro SG90 Servo Motor is used to open/close the smart bin lid automatically. The Arduino Uno microcontroller was used to control the smart bin overall functionality. The smart bin separates dry/wet garbage collection using IoT sensor within the smart bin. The smart bin is full up to 80% then the microcontroller sends warning message to garbage collector automatically. The top of the smart bin solar panel is fixed, which is used to collect energy from sun. The collected energies are stored in the battery bank which is used to operate smart bin automatically. The applications of smart bins are shopping malls, Colleges/University campus, Railway stations, Tourist areas, Parks etc.

Consensus Analysis of Connected Vehicle System with Stochastic Multiplicative Noises

As an application of the Internet of Things, the connected vehicle system (CVS) plays an important role in smart city. While the vehicles in a CVS inevitably suffer with environmental noise interference, which affect the stability of the whole system. So in this paper, the consensus of autonomous CVS is researched in the presence of stochastic multiplicative noise. The parameter restriction method is first proposed which achieves the control requirements by restricting the parameters of the designed control protocol. Then we optimize this method so that it can resist the noise with slightly larger intensity. However, this method has limited ability to resist noise of large intensity. Next we propose another method that is called the interaction network-based method, which can ensure the convergence of the system through introducing a virtual system that interacts with the original CVS. By selecting parameters of the interaction network properly, such method has a good performance in resisting larger intensity noise. In the mentioned two methods, the convergent results are derived to ensure the stability of the platoon by using the stochastic differential equation(SDE). Finally, the validity of the proposed methods is verified in the designed simulations.

A human-centered artificial intelligence approach for privacy protection of elderly App users in smart cities

Artificial Intelligence and Machine Learning based Ambient Assisted Living systems play an important role in smart cities by improving the quality of life of the elderly population. Many Ambient Assisted Living systems are coupled with Android Apps for command-and-control purposes. Consequently, the privacy and security of Ambient Assisted Living systems depend on the privacy and security of the corresponding Android Apps, which follow a privacy self-management model. Unfortunately, the privacy self-management model ignores the decision-making abilities of the elderly and increases their cognitive loads, which put their privacy protection and wellbeing at stake. In this paper, we follow a Human-Centered Artificial Intelligence inspired approach for addressing these issues. This approach uses privacy as a shared responsibility model instead of the privacy self-management model. We have proposed two algorithms, the participatory privacy protection algorithm-I, and participatory privacy protection algorithm-II, for determining optimal privacy settings of an Ambient Assisted Living App and handling its runtime Permission requests, respectively. We demonstrated the working of these algorithms using a case study. We have also compared the proposed approach with state-of-the-art privacy management schemes for Android Apps. The proposed algorithms can improve the privacy protection of Ambient Assisted Living App users in smart cities and relieve them through cognitive offloading.

Internet of Thing Based Smart Traffic Control Signal Using Solar Energy

Nowadays traffic congestion is major problem in all over the cities. The cities are renovated to “smart cities” by using Information and Communication Technologies (ICT). The IoT are playing a vital role in smart cities. This work proposes Internet of Thing (IoT) based smart traffic control signal using solar energy for smart cities. This signal is always coordinated with the emergency vehicle like ambulance to discover the signal and select the route where road traffic is dynamically controlled and the traffic violation vehicles are identified by traffic monitoring officers through internet. The traffic control signal lights are automatically controlled by Raspberry Pi controller with the help of IR sensor and RF signal. If any emergency vehicle will come nearby traffic control signal then the green signal shows for emergency vehicle and the remaining paths are shows as red signal.

Role of Smart Cities in Optimizing Water-Energy-Food Nexus: Opportunities in Nagpur, India

The evolving concept of ‘Smart Cities’ (SC) is today gaining global momentum in scientific and policy arenas. With the rising potential for jobs and improved lifestyle, these fast-growing cities are witnessing an ever-increasing concentration of populations and economic activities. However, the core aspect of sustainability is often overshadowed in SC by the components of economic growth. In view of the rising water, energy, and food (collectively referred to as WEF) demands in cities and the interlinkages between WEF systems, this study aims to highlight the role of SC in enhancing WEF resource efficiency from a nexus perspective. To study the current trends of SC developments first, a specific case of proposed Nagpur smart city (India) is reviewed based on document analysis and preliminary discussions with local government officials. Thereafter, bibliometric analysis (based on Scopus data) is conducted to establish a comprehensive understanding of existing SC research in varying domains of interlinked WEF systems. While the proposed Nagpur smart city is observably focused on city-centric goals with a high emphasis on infrastructure development, this study underlines the need for environmental resource conservation at a transboundary level. The key opportunities for optimizing the WEF nexus in Nagpur are then highlighted in reference to the bibliometric analysis.

Cascading Scene and Viewpoint Feature Learning for Pedestrian Gender Recognition

Pedestrian gender recognition plays an important role in smart city. To effectively improve the pedestrian gender recognition performance, a new method, called cascading scene and viewpoint feature learning (CSVFL), is proposed in this article. The novelty of the proposed CSVFL lies on the joint consideration of two crucial challenges in pedestrian gender recognition, namely, scene and viewpoint variation. For that, the proposed CSVFL starts with the scene transfer (ST) scheme, followed by the viewpoint adaptation (VA) scheme in a cascading manner. Specifically, the ST scheme exploits the key pedestrian segmentation network to extract the key pedestrian masks for the subsequent key pedestrian transfer generative adversarial network, with the goal of encouraging the input pedestrian image to have the similar style to the target scene while preserving the image details of the key pedestrian as much as possible. Afterward, the obtained scene-transferred pedestrian images are fed to train the deep feature learning network with the VA scheme, in which each neuron will be enabled/disabled for different viewpoints depending on whether it has contribution on the corresponding viewpoint. Extensive experiments conducted on the commonly used pedestrian attribute data sets have demonstrated that the proposed CSVFL approach outperforms multiple recently reported pedestrian gender recognition methods.

Smart and Age-Friendly Cities in Romania: An Overview of Public Policy and Practice.

The role of smart cities in order to improve older people’s quality of life, sustainability and opportunities, accessibility, mobility, and connectivity is increasing and acknowledged in public policy and private sector strategies in countries all over the world. Smart cities are one of the technological-driven initiatives that may help create an age-friendly city. Few research studies have analysed emerging countries in terms of their national strategies on smart or age-friendly cities. In this study, Romania which is predicted to become one of the most ageing countries in the European Union is used as a case study. Through document analysis, current initiatives at the local, regional, and national level addressing the issue of smart and age-friendly cities in Romania are investigated. In addition, a case study is presented to indicate possible ways of the smart cities initiatives to target and involve older adults. The role of different stakeholders is analysed in terms of whether initiatives are fragmentary or sustainable over time, and the importance of some key factors, such as private–public partnerships and transnational bodies. The results are discussed revealing the particularities of the smart cities initiatives in Romania in the time frame 2012–2020, which to date, have limited connection to the age-friendly cities agenda. Based on the findings, a set of recommendations are formulated to move the agenda forward.

Strategies for developing tube houses in Vietnam within the framework of “smart city” concept

The becoming of smart cities is an inevitable trend for future cities. Vietnam is in the rapid urbanization process facing many challenges. Smart cities require many criteria, in which providing a convenient living environment for citizens is the top priority. The current situation of urban housing in Vietnam shows a unique characteristic, in which tube houses occupy a large proportion and offer a low quality living environment. This type of construction will play a major role in smart cities. The objective of this paper is to determine the strategies for developing future tube houses within smart cities of Vietnam. Methods used in the paper include: data collection method, analytical – synthetical method and logical method. Results of the paper are shown using appropriate development strategies with necessary criteria and general architectural solutions. In addition, there will be two directions of design solutions: a) renovating of tube houses at existing residential areas and b) new construction of tube houses in new areas. The conclusion drawn from this article concerns designing and building tube houses within the framework of a “smart city” concept. These proposals must conform to practical requirements and should be implemented step by step to ensure sustainable development.

Smart governance based on multipurpose territorial cadastre and geographic information system: An analysis of geoinformation, transparency and collaborative participation for Brazilian capitals

In the information era, cities are connected by data networks and, therefore, solutions to urban problems have been supported by Information and Communication Technologies, which provide the interaction between government and citizens. Thus, information plays a crucial role in Smart Cities, enabling the smart governance, under the precepts of good governance, highlighting the participation and transparency. In addition, Geoinformation turns the Multipurpose Territorial Cadastre into a decision support informational source that is of great relevance in the context of Smart Governance. Based on this context, the present study investigates the geotechnology scenario applied by the governments of the Brazilian capitals, observing the availability of Multipurpose Territorial Cadastre on the Internet and considering the Geoinformation, Transparency and Collaborative participation aspects. In this sense, this paper proposes an exploratory research, with quantitative and qualitative analyzes. In the first stage, it was identified the cities that offer access to territorial information via the Internet, i.e., quantifying cities with active geoportals. Next, the second step qualifies the geoportals of cities according to their existence and access to certain information. The types of information selected for the survey, considered the evolution of the Cadastre, observing three aspects: (i) cadastral information; (ii) thematic information; and (iii) collaborative interface. The results showed that just over half of the capitals keep active geoportals and almost 1/3 of the capitals make available on the Internet a Multipurpose Territorial Cadastre. In terms of Transparency, it was noted that open access data is still lower than non-available or restricted access data. Moreover, the aspect of Collaborative participation is meaningless. Analyzing the results, it can be noted the need to improve both the availability of data and open access to information regarding the aspects of Multipurpose Territorial Cadastre in the geoportals, mainly the cadastral information. These actions could promote the transparency and collaborative participation that, consequently, will improve the effectiveness of smart governance in the Brazilian capitals.

Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities.

With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

Trustful Internet of Surveillance Things Based on Deeply Represented Visual Co-Saliency Detection

Trustful Internet of Things (IoT) plays an important role in smart cities. The trust information in surveillance data motivates the analysis of images from numerous IoT devices. Saliency detection is a fundamental step in surveillance data analysis for providing help to the subsequent tasks, but unsuitable to IoT applications owing to the neglect of image similarity and difference from diverse IoT devices. To solve this problem, we enable the co-saliency detection in IoT, which detects the common and salient foreground regions in the group surveillance images. The main contributions include: 1) enable a multistage context perception scheme to efficiently extract the contextual information corresponding to different-size receptive fields in the single image; 2) construct a two-path information propagation to extract the interimage similarity and difference from the high-level image feature representations of the group images; and 3) propose the stage-wise refinement to allocate the label information to different parts of the network for helping the network to learn the enriched semantically common knowledge. The extensive experiments performed on three public data sets can demonstrate the effectiveness of our approach and its superiority to four state-of-the-art co-saliency detection methods.

Key Establishment Algorithm for Secure Cyber Physical System to Prevent Cyber Attacks

Extensive research effort is going on to contribute to the Cyber Physical system (CPS). It will create a physical system capable of computation, communication and sharing information between physical devices. Physical devices become smart devices because of CPS. CPS are engineered systems that are built with sensors, microcontrollers which performs computation, communication, control, process and analysis of physical objects. CPS playing important role in smart cities, home automations and other applications. Due to resource constraints like low processing power and low storage space there is a lack of standard security mechanism which can provide adequate security in CPS. In the research paper, we proposed Key establishment mechanism which prevents CPS from cyber-attacks. The goal of this research paper are six -folds: i) to give a detailed overview of cyber physical system. ii) To study architecture of CPS. iii) To study different attacks on CPS with attack points. iv)Application areas of CPS v) security threats in CPS vi) Key establishment algorithm to prevent cyber-attacks in CPS.

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

AbstractDigital transformation played a vital role in smart cities because of its ability to process the different data to providing sustainability, connectivity, and mobility to data effectively. According to the diver operational productivity, preserving public safety government law enforcement integrated that face recognition is more important in smart cities. Traditional face recognition system fails to predict the exact facial features that leads to reduce the facial recognition accuracy. The false facial point detection process maximizes the computation complexity. Therefore, in this work, effective and artificial intelligence internet of things–based facial expression detection system is implemented to predict and match the face from the database. Initially, the facial images are captured from the internet of things sensor device, which is processed by applying the Perona‐Malik diffusion algorithm. Then, face location is cropped from the image, geometric face shape model is created for predicting the exact face from the template face image. From the face shape, different facial features are extracted from various region using Fisher linear discriminant analysis. The derived features are trained with the help of convolution network and the face recognition process is done by using the adaboost large memory storage and retrieval neural network. The network successfully recognizes the face from the template, which is used to eliminate the safety related risk in smart cities.

Sensing the disturbed rhythm of city mobility with chaotic measures: anomaly awareness from traffic flows

Big data-driven intelligent transportation plays an important role in smart cities. Moreover, upcoming abnormal events threatening to public safety can be altered prior to their appearance since such events break the regular rhythm of city mobility patterns. The purpose of this study is to detect and forecast abnormal events from the pulse of traffic flows. Specifically, information entropy, Boltzmann entropy, and fractal dimension are used to calculate the degree of the disequilibrium regarding how vehicles distribute on the transportation network. Then, the experiments were conducted based on simulated data and GPS traces of taxies in Shanghai, China. The results show that the proposed method can accurately indicate abnormal events to appear in reality. Finally, a comparison of the advantages and disadvantages of the three chaotic measures leads to insight into the rhythm of city mobility.

Climate Change and Decentralized Renewable Energy: The Role of Smart Cities

This article, first and foremost, aims to frame the climate change phenomenon in the international context in order to highlight the impact of public energy policies on global warming. Than the article focuses on analysing the provisions of the Directive 2018/844/ EU and the centrality foreseen in it, of energy efficiency and therefore on capturing the role of local governments in designing cities. The smart cities model, combined with the transition of electricity generation from a centralized model to a distributed one, promotes various virtuous scenarios in terms of pollution’s containment. Therefore, it would be appropriate to provide political support in terms of economic incentives, aimed to encourage the bottom-up thrust, promoting investments aimed to develop smart cities and to create new small energy producers, which represent the natural and physiological framework of the new distributed and decentralized energy model.

LCC: Towards efficient label completion and correction for supervised medical image learning in smart diagnosis

Smart technology brings convenience to our lives, and smart medical diagnosis plays a particularly important role in smart cities and the smart world. In smart diagnosis, high-quality image labels are essential for supervised medical image learning. The correctness of labels has a substantial influence on smart disease diagnosis. However, the labelling of disease images by professional doctors is a time-consuming and labour-intensive project; therefore, obtaining correct and high-quality labels is a difficult task. This paper proposes a deep convolutional neural network (CNN)-based label completion and correction method (LCC) for supervised medical image learning in smart diagnosis. We use a small number of labelled images in a dataset to train a model, and design a strategy to complement most of the unlabelled data and correct noise label data, and then use the results of completion and correction to continually modify the model to achieve better results. We take the diagnosis of Seborrheic Keratosis (SK) and Flat Wart (FW) as examples; the experimental results show that this strategy can use limited labelled data to complete the labels for most of the unlabelled data and correct some noise labels in the dataset, which improves the accuracy rate of the model to identify diseases.

Environment and Big Data: Role in Smart Cities of India

The intention of India’s Smart City Mission program is to achieve better living conditions in a sustainable environment with smart solutions. This program identifies the key challenges of urbanization and the environment. The mitigation of these challenges depends on the monitoring and assessment of multiple factors, including demography, education, health, and the environment; however, the inclusion of environmental factors are limited. The monitoring and assessment of environmental factors will continuously generate big data and hence would require scientific and technological innovation for a sustainable management plan. This study identifies six environmental factors, which should be integrated in the development of smart cities. These environmental factors include indicators of landscape and geography, climate, atmospheric pollution, water resources, energy resources, and urban green space as a major component of the environment. This paper also discusses the importance of these environmental components and the maintenance of big data in the management of smart cities.

Optic disc segmentation and classification in color fundus images: a resource-aware healthcare service in smart cities

The services of health care play an important role in smart cities, which is the future reality of the current age. The automatic recognition of the Optic Disc (OD) region is an essential step in automated retinal image analysis. Generally, OD needs to be excluded for detection of exudates, the major symbols of diabetic retinopathy. The mobile-cloud assisted classification of OD from color fundus images can improve classification accuracy and OD’s recolonization speed. In this research, we propose a smart-phone based cloud-assisted resource aware framework aimed to detect and classify OD from fundus images by using a well trained classification procedure in cloud. The proposed technique comprised of shape based features trained by Naive Bayes classifier in the cloud for OD and non-OD class. Experiments are performed on three online available datasets and a real dataset developed at local hospital with different contrast, illumination and abnormality. The proposed technique achieves an accuracy of 98.25% in classification of OD.