Abstract

Keywords

Introduction

Smart electric pole

Sustainable power unit

Conclusion

Data Availability Statement

Declaration of Competing Interest

References

Abstract

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also.

Introduction

Sustainable energy is the method of utilizing energy in an efficient way and at the same time satisfying the needs of the modern world. Sustainable energy can be used in economic, social and environmental issues. An efficient sustainable energy management system can be achieved by using renewable resources in all the possible ways, using sustainable energy resources, using alternative energy resources, making use of smart systems, implementing IoT systems, implementing green technologies. The smart system is the technique, which is used to embed the modern technology into functions already used by the world. In terms of smart system, the information about utility system and traffic flow of the large cities is extracted with the help of sensor also it analysis the pattern of information for prediction. Then, the ability to transfer data over a network without the help of human-to-computer or human-to-human interaction is called IoT or internet of things system and the green technology is used to explain the functions of science and technology to develop products as environmental friendly. Some of the examples of green technology is water purification, conserve natural resources, clan energy creation and waste recycle management. It is also used in digital and mechanical machines, objects and in correlated computing devices.

Abstract

Open Access and Permissions

Share and Cite

Article Metrics

Related Articles

Order Article Reprints

Abstract

In recent years, the world’s population living in cities has been rapidly increasing. Cities are transforming their infrastructure in a smarter and more efficient way so that sustainable development forms part of their long-term strategy. However, this transformation does not always result in expected benefits due to a variety of factors such as an absence of social acceptance, a lack of holistic design and the development of unilateral interventions. An analysis of the scientific literature related to the evaluation of the impact of smart city actions revealed a gap in the holistic methods for their assessment. To this end, an accurate evaluation of implemented smart solutions focusing on the energy domain is necessary in order to assess the expected and realized impact of each solution. This paper proposes a seven-step methodology for assessing the impact of smart city interventions and presents a use case for the city of Espoo. A number of major findings were the outcome of our research and development work, such as the need for a thorough analysis of the long-term vision of the city, a combined top-down and bottom-up approach and the ongoing cooperation between all stakeholders involved in urban planning and transformation, in which necessary Key Performance Indicators (KPIs) are defined.

1. Introduction

1.1. The Necessity of Impact Assessment
According to the United Nations [1] 68% of the world’s population is projected to live in urban areas by the year 2050; cities, therefore, are anticipated to face new challenges in sustainably integrating further populace, such as a lack of appropriate infrastructure for sustainable energy and automated systems, applications and commercial arrangements, digital security, network connectivity and data capacity [2]. Cities will be required to transform their infrastructures in a smarter, more efficient and resilient way so that sustainable development forms part of their long-term strategy and a better quality of life is assured to their citizens. The concept of the smart city is multidimensional [3], with the aim of increasing urban sustainability and quality of life while pursuing economic development among its citizens [4]. Additionally, sustainability has gained popularity among scholars, environmental policy makers and government agencies [5]. However, a smart city is not just about creating technically viable urban areas and stimulating economic growth; it requires a multifaceted and multidimensional approach that promotes a structural transformation in the urban ecosystem, directing the development of cities towards their “smartness” in areas such as economy, governance, social, environment, mobility and ICT [6].

Abstract

Keywords

1. Introduction

2. Related work

3. Evaluation system of urban green space landscape planning based on PSO-BP neural network model

3.1. Application feasibility and evaluation index of neural network in urban green space landscape planning scheme evaluation

3.2. Network construction of urban green space landscape scheme evaluation system

3.3. Network optimization of urban green space landscape scheme evaluation system

4. Training results and simulation results of urban green space landscape scheme evaluation system

4.1. Training results of PSO-BP neural network system for urban green space landscape scheme evaluation

4.2. Evaluation and analysis of PSO-BP neural network system for urban green space landscape scheme evaluation

5. Conclusion

Funding Statement

Declaration of Competing Interest

References

Abstract

The evaluation simulation of urban green space landscape planning scheme based on PSO-BP neural network model is carried out in this paper. PSO-BP neural network can combine the principle of landscape ecology, integrate more evaluation indicators of ecology and urban development into the urban green space landscape planning scheme, and simply understand and predict human behavior, so as to make a more comprehensive evaluation and prediction of the urban green space landscape planning scheme. It not only has superior memory storage and learning ability, but also can simply understand and predict human behavior, so that more influencing factors that cannot be added in the past can be considered in the scheme evaluation and analysis, and the evaluation of urban green space landscape planning scheme is more comprehensive, scientific and reasonable. Experiments show that PSO-BP neural network has smaller error and better generalization ability than BP neural network. PSO-BP neural network rating model can analyze its more reasonable proportion according to the relationship between different types of green space and indicators, and give corresponding adjustment suggestions, which has guiding significance for the modification and adjustment of urban green space landscape planning scheme.

1. Introduction

With the continuous advancement of urbanization in China, more and more cities ignore the planning and development of urban landscape and the impact of urban green space landscape on the living standard and quality of urban residents,

With the continuous improvement of China’s comprehensive strength and international status, the degree of urbanization in China has been greatly improved, and more and more urban construction is close to fashion cities. But at the same time, many cities ignore the planning and development of urban green space landscape and the impact of urban green space landscape on the living standards and quality of urban residents, resulting in the continuous reduction or even lack of urban green space landscape area, the simplification of urban landscape and the imbalance of ecological environment, Finally, it leads to the contradiction between the urban environment and the quality of life of urban residents [1], [2]. Urban green space landscape planning has also become an important part of urban planning and development, and is an issue of increasing concern to urban residents [3]. With the improvement of China’s economic level, the material life of urban residents has been basically met, the urban living environment has gradually become a problem of increasing concern to urban residents, and the urban green space landscape planning has also become an important part of urban planning and development [3].

Abstract

Introduction

Review of literature

Methodology

Integrated smart city model

Conclusion

Appendix

References

Abstract

     In smart city architecture, information and communication technologies are used to improve living standards and its management by citizens and government. Most researchers have divided this structure into six main components: smart people, the smart government, smart environment, smart transportation, smart economy, and smart life. Due to the connection between smart cities and the challenges resulting from their implementation and especially its integration, there exists no perfect solution for the concept of an integrated smart city so far according to our studies. Some more general concepts such as security, ICT infrastructure, and knowledge are not seen integrative in these structures. Therefore, it seems that new sub-components and general extra-components should be added to the existing models to form an integrated structure in such a way that the executive projects are located in their proper place in this structure and create and guarantee the integration of the smart city. Therefore, the requirements engineering of the smart city can also be explained more precisely. This study presents a model of an integrated graph in such a way that besides maintaining and improving the model of the smart city and existing models, it will fully cover the integration and requirements engineering and methodologies of the smart city in the future.

Introduction

     The process of urbanization is currently taking place at a rapid pace on a global scale. Every year, millions of people migrate from rural to urban areas to gain comfort and economic opportunities. According to the United Nations (UN), 6. 5 billion people will live in cities by 2050, and more than 60% of the world’s population will be concentrated in cities (Schuurman and Baccarne 2012). On the global scale, International Data Corporation (IDC) estimates that the steady increase in smart cities’ expenses will rise to $ 158 billion by 2022 (Twice as much as $ 80 billion in 2018) (Visan and Ciurea 2020). Rapid population growth, besides putting considerable pressure on current cities, will pose serious threats to the future, leading to increased demand for services, housing facilities beyond the capacity of cities, as well as irreparable damage to the environment. As a result, cities face many challenges as their resources and infrastructure increase under increasing pressure (Breetzke 2016). Therefore, they always suffer from adverse consequences. Also, the sudden emergence of problems such as the recent Panorama of Covid-19 as well as advances in telecommunications, especially the Internet, along with the development of electronic devices, have changed people’s everyday life and their minds in all aspects of economic and social fields (Das and Zhang 2020).

Conclusion

     In previous research based on the current situation and thematic literature, the concept of smart city was mostly proposed and implemented as an island with six main components in different societies. Also due to the increase in urbanization and the connection of smart cities with the challenges that arise in implementing and integrating the urban model, a suitable solution for the concept of integrated smart city Was not provided to your best knowledge and some essential concepts such as security, ICT infrastructure and knowledge are not seen integrative in these structures. In order to integrate, prevent data redundancy, ease of access, create security at different levels, the present study Provides a flexible, integrated, localizable dynamic model with a new approach that breaks down six core components into several sub components that creates a graph-like model, in which each sub component represents a set of implementable smart projects in different communities.

Introduction

One of the most noteworthy arcs of the 21st century has been the explosive growth seen in the usage and deployment of technology. More specifically, the advancement of information and communications technology (ICT) has been the foundation for many of the innovations seen over this century thus far. The wide-spanning adoption of the Internet for social media, e-commerce, news consumption, and more has redefined much of our everyday lives and industries across the world. Due to the advancements of ICT, there are many exciting opportunities for what is yet to come. One longstanding opportunity for the future is the vision of the smart city.

There are countless different ideas and definitions for what exactly qualifies as a smart city. A more general definition, provided by Bakıcı et al. in [1], defines smart cities as being high-tech cities that serve to connect people, information, and city-wide infrastructure and resources using advances in technology to improve sustainability, innovation, and overall quality-of-life. The extent to which how pervasive (or intrusive) technologies are incorporated to accomplish such goals is often a point of contention in the literature [2]. However, the many different visions for what define smart cities all generally involve heavy utilization of technology to improve the maintenance of cities to further improve and/or optimize city-specific operations for different stakeholders (e.g., citizens, the environment).

Realizing the ambitious vision of smart cities requires seamlessly making decisions for a host of different applications and domains with minimal sacrifice with respect to performance and resource costs. Some applications of interest for smart cities include, but are not limited to, smart traffic light control, smart information spread (or advertisement), smart energy distribution, smart waste management, etc. Ideally, data generated in urban environments can be collected in smart cities using CT-powered infrastructure to train and inform models designed for these applications to make realtime decisions. For instance, insistent collection of traffic congestion data from roadside units(RSU) and other sources can be sent to ICT infrastructure to train machine learning models how to make optimal decisions related to traffic lights to reduce congestion. This approach is becoming more within reach given the advances in vehicular ad hoc networks (VANET) [3, 4, 5] and other Internetof-Vehicles (IoV) systems [6, 7, 8].

Making smart decisions in a timely fashion will require a powerful pipeline ready to support intense amounts of data collection, analytics, and adaptive decision-making. For instance, video feeds at road intersections — to empower models for smart traffic light control — are large in size (in terms of bytes) and will require automated processes to make meaningful decisions in real-time. However, video cameras that provide such video feeds will be unable, on their own, to perform such analytics and decision-making. Such tasks would require more computational resources than would be available at video cameras and other similar sensors that can be found in urban environments. Thus, for such applications, smart cities will need to be able to provide heavy computational power on-demand, even for devices that do not have such resources locally. Applications (e.g., big data analytics, video classification) all require large amounts of compute resources in order to adequately process. Because Internet-of-Things (IoT) devices like smartphones are often ill-equipped to meet the needs of such processes [9, 10], a standard approach for providing these services on these devices is to rely on remote resources.

Abstract

Document Sections

I. Overview of the Framework

II. Background Works

III. Proposed Framework

IV. Consensus Algorithm and Flowchart of Proposed Framework

V. Results and Discussion

VI. Conclusion

Abstract:

This manuscript describes since the resources are heterogeneous, a smart city is vulnerable to several security attacks, and to devise an effective response, it is necessary to recognize certain risks and their potential implications. The framework is responsible for the data transfer security issues in the smart city to incorporate and maintain physical, social, and industrial systems to provide high-quality secure data transmission to its residents. The proposed method of integrating the Blockchain and Internet of Things (IoT) with a consensus algorithm framework (BCIoT-CAF) to overcome the issues and secure data sharing in smart cities. The data security is enforced by splitting the blockchain network into separate networks, with any channel consisting of a limited number of approved entities, the data stored in the cloud gate server, and analysis of data achieved by IoT applications. The consensus algorithm plays an important role in retaining the blockchain’s speed, protection, and performance. Using an appropriate security algorithm, blockchain applications may greatly improve their performance. The modeling simulation of this research improves the speed, protection, and efficiency of 96% and tolerance error as 55%. The integrated evaluation results suggest that the proposed system integrates blockchain with IoT to provide a shielded communication platform in smart cities.

I Overview of the Framework

Blockchain used to share the data information securely and avoid any other fraudulent data threats[1]. IoT system implementations can be challenging and a transmitting ledger ideal for defining, authenticating, and protected data securely from IoT devices [2],[3]. Blockchain and IoT are two innovations rising in importance since they started. IoT can affect nearly ordinary things shortly. In the increased use of this tool, the risk of violence decreases [4]. To cope with this, existing techniques are not adequate. Blockchain has proven to be an innovative approach to IoT security problems and is a distributed network that relies on a consensus algorithm, ensuring agreement between distributed nodes on the status of some data [5],[6]. The central factor of the consensus algorithm that specifically defines a program operates and its output [7]. Blockchain technology attracting more coverage, revolutionize, automate the digital network of internet-based technologies [8]. This is a shared database that tracks each transaction that takes place on a network[9],[10]. It has a block spread over a node network. The consensus algorithm is a mechanism in which all the blockchain system peers decide on the existing state of the decentralized directory [11]. This ensures that consensus algorithms build stability in the blockchain network and confidence in the distributed computing system among unidentified peers [12]. The consensus protocol guarantees that a new block stored on the blockchain is the valid iteration accepted by all of the blockchain peers [13], [14].

Highlights

Abstract

Keywords

1. Introduction

2. Methods and materials

3. Results and findings

4. Discussion

5. Conclusion

Author statement

Declarations of Competing Interest

Acknowledgements

Appendix. : Review of selected articles in each research topic

References

Vitae

Abstract

There have been many attempts to transform cities into smart cities worldwide. However, it is difficult to understand and describe smart cities from different perspectives, given the widespread application of the concept of smart city in diverse disciplines, such as urban planning, electronic engineering, and computer sciences. This work conducted a comprehensive smart city literature review based on text mining of 3,315 papers on smart cities published in journals indexed in the Science Citation Index Expanded and Social Sciences Citation Index databases. These include “all papers” classified as research articles published from 1999 to April 2020. Our findings show the state of the art of research on smart cities, including (i) smart city literature statistics from 1999 to 2019, (ii) 23 research topics related to smart cities, and (iii) geographical variations in smart-city research. Based on these findings, we offer theoretical and practical implications of (1) missing fields of studies, (2) future research directions, and (3) the applicability of text-mining techniques to literature reviews. We believe that this work, which aims to establish a common ground for understanding smart cities from multiple disciplinary perspectives, will encourage further research and development regarding smart cities.

1. Introduction

With the development of Artificial Intelligence (AI) and Internet of Things (IOT) technology, the idea of smart cities is attracting attention as a new growth engine and development strategy in many global cities. Many cities are introducing new urban services, using information and communications technology (ICT) to build smart cities that improve the quality of life of citizens. However, despite its obvious importance and popularity, there is a lack of in-depth understanding of smart cities in the literature, and the concept of “smart city” remains unclear. What is a smart city? What are the major smart city research areas and how are they related? What are the key technological factors of smart cities? What are the main application areas and how do they vary in different countries? In short, how should we describe “smart cities”?

A comprehensive review of smart cities across different fields may facilitate smart city planning and development. However, such integrative work is not easy to achieve because of the variety and volume of studies and applications related to smart cities. One of the commonly applied methodologies for integrative work is the bibliometric approach, which allows to identify how a specific concept or research discipline is formulated and how it correlates with other concepts by clarifying the relationships between all the concepts (Kalmaski and Kirby, 2012).

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings’ energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

1. Introduction

Buildings such as residential, education, office, healthcare, and industrial are emerging as critical consumers in energy consumption. Energy consumption for buildings represents 30–45% of global energy use [1–3], with a larger part of the energy used by the building subsystems, which consist of cooling and heating systems; safety, water, lighting, and similarly combined subsystems. In this context, efforts at this time are focused on the fulfillment of the requirements for energy-efficient in buildings, by guaranteeing the operative needs with the base conceivable energy cost and environmentally friendly [4]. In many developing and developed countries, energy efficiency is viewed as the best mechanism to address and defeat ever-rising energy needs [5]. In any case, advancing the energy efficiency of these subsystems is very testing since they typically have to comply with complex working requirements, dynamic energy necessity, and comfort needs [6].

1- Introduction

2- Related work

3- Proposed architecture

4- Results: Two citizen-oriented case studies

5- Conclusions and future work

References

Abstract

Today’s society must prioritize the design and development of platforms for Big Data processing. Smart cities generate large volumes of valuable data which the government can use to manage cities more intelligently. Aware of the value of data, many researchers have proposed architectures for optimized data collection and use. This paper proposes a novel approach that enables smart cities to reuse the functionalities of old applications by adapting them to new architectures. To be able to process large amounts of data and solve a variety of problems, smart city platforms need extra computing power. Two case studies have been conducted to verify the performance of the proposed platform. Those case studies have demonstrated that the development process of a smart platform can be simplified by implementing functionalities and components taken from earlier platforms.

Introduction

Smart cities promote a sustainable lifestyle. Infrastructure, innovation and technology are the components that make smart cities efficient and self-sufficient. Examples of sustainability measures include the use of photovoltaic panels to provide electricity to buildings, street signs or electric vehicles. In addition, wind turbines are used to power street lights and the use of bicycles is encouraged as a means of moving around the city etc. The degree of sustainability of smart cities is measured according to a series of parameters, including: public management, social cohesion, governance, technology, urban planning, environment, mobility and transport, international projection, human capital and economy. Smart cities have got a huge variety of applications to monitor and control each of those parameters [1]. However, one of the barriers to efficiency is that different companies use different smart city applications to offer services to citizens. This can cause communication problems and can therefore hinder efficiency. Moreover, demand for new services is continuous in smart cities, so companies that wish to provide new services must make new developments instead of reusing and customizing the old ones.