Including Smart Cities Research Articles

SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network

The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach for securing fitness IoT applications. The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring, analysis and response system. The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks. The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actuator node within the fitness IoT system. SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system. The SEN calculates the inbound and outbound traffic features’ entropies and transmits them to the Blockchain in the form of transaction blocks. The BEP picks the whole mined blocks’ transactions and transfers them to the SDN controller node. The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack. So, there are two decision points, one is SEN, and another is the controller. To evaluate the performance of our proposed system, several experiments are performed and results concerning the entropy values and attack detection rate are obtained. The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11% and 18% against Approach 1 and Approach 2 respectively.

Prediction Models to Effectively Detect Malware Patterns in the IoT Systems

The Widespread use of the Internet of Things (IoT) has influenced many domains including smart cities, cameras, wearables, smart industrial equipment, and other aspects of our daily lives. On the other hand, the IoT environment deals with a massive volume of data that needs to be kept secure from tampering or theft. Detection of security attacks against IoT context requires intelligent techniques rather than relying on signature matching. Machine learning (ML) and Deep Learning (DL) approaches are efficient to detect these attacks and predicting intrusion behavior based on unknown patterns. This study proposes the application of five deep and ML techniques for identifying malware in network traffic based on the IoT-23 dataset. Random Forest, Catboost, XGBoost, Convolutional Neural Network, and Long Short-Term Memory (LSTM) models are among the classifiers utilized. These algorithms have been selected to provide lightweight security systems to be deployed in the IoT devices rather than a centralized approach. The dataset was preprocessed to remove unnecessary or missing data, and then the most significant features were extracted using a feature engineering technique. The highest overall accuracy achieved was 96% by applying all classifiers except LSTM which recorded a lower accuracy.

Contemporary Urban Missions and Reflecting Reality in Deprivation of Civil Areas in Indian Cantonments – A Pragmatic View

The contemporary urban schemes were launched around five years ago by the National Government to create more inclusive cities and offer a decent quality of life to urban residents. But in reality, the civilian areas of Cantonments are grossly overlooked from the benefits of such welfare schemes. There are 52 notified civil areas in Indian Cantonments with a population of 2.08 million, according to the 2011 census. The Cantonment Act, 2006 (by repealing the Cantonment Act, 1924) empowered Cantonment Boards to act as ‘deemed to be a municipality ’to receive grants and implement government welfare schemes, including the provision of 24 types of infrastructure and services to its residents. The present research reviews the provisions and coverage of contemporary urban missions, including Smart Cities, and highlights civilian areas of the cantonments as deprived urban areas. Besides the non-implementation of contemporary urban welfare schemes, issues like the age-old colonial infrastructure, revenue crunch through taxes and non-taxes, absence of development plan, lack of inter-jurisdictional coordination etc., need to be addressed. The present research will act as an input for policymakers to understand the problems of civilian areas, nature, and extent of welfare scheme implementation, and also suggest the necessary changes required at the policy level.

Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) became essential in developing many applications, including smart cities and Internet of Things (IoT) applications. WSN has been used in many critical applications such as healthcare, military, and transportation. Such applications depend mainly on the performance of the deployed sensor nodes. Therefore, the deployment process has to be perfectly arranged. However, the deployment process for a WSN is challenging due to many of the constraints to be taken into consideration. For instance, mobile nodes are already utilized in many applications, and their localization needs to be considered during the deployment process. Besides, heterogeneous nodes are employed in many recent applications due to their efficiency and cost-effectiveness. Moreover, the development areas might have different properties due to their importance. Those parameters increase the deployment complexity and make it hard to reach the best deployment scheme. This work, therefore, seeks to discover the best deployment plan for a WSN, considering these limitations throughout the deployment process. First, the deployment problem is defined as an optimization problem and mathematically formulated using Integer Linear Programming (ILP) to understand the problem better. The main objective function is to maximize the coverage of a given field with a network lifetime constraint. Nodes’ mobility and heterogeneity are added to the deployment constraints. The importance of the monitored field subareas is also introduced in this paper, where some subareas could have more importance than others. The paper utilizes Swarm Intelligence as a heuristic algorithm for the large-scale deployment problem. Simulation experiments show that the proposed algorithm produces efficient deployment schemes with a high coverage rate and minimum energy consumption compared to some recent algorithms. The proposed algorithm shows more than a 30% improvement in coverage and network lifetime.

Applications, Deployments, and Integration of Internet of Drones (IoD): A Review

The Internet of Drones (IoD) has become a hot research topic in academia, industry, and management in current years due to its wide potential applications, such as aerial photography, civilian, and military. This paper presents a comprehensive survey of IoD and its applications, deployments, and integration. We focused in this review on two main sides; IoD Applications include smart cities surveillance, cloud and fog frameworks, unmanned aerial vehicles, wireless sensor networks, networks, mobile computing, and business paradigms; integration of IoD includes privacy protection, security authentication, neural network, blockchain, and optimization based-method. A discussion highlights the hot research topics and problems to help researchers interested in this area in their future works. The keywords that have been used in this paper are Internet of Drones.

The role of low‐power wide‐area network technologies in Internet of Things: A systematic and comprehensive review

SummaryWith the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB‐IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery‐powered IoT objects that enable long‐distance communication in low‐power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.

Blockchain-based federated learning methodologies in smart environments.

Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.

Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework

Whilst a plethora of research exists on the smart cities and project performance evaluations, only few studies have focused on the smart city policy evaluation from the perspective of its acceptance by practitioners. This paper aims to generate insights by evaluating the smart city policy through a developing country case study—i.e., Malaysia. This study employed a questionnaire survey method for data collection and analyzed the data by using Fuzzy Delphi analysis. A group of 40 practitioners was gathered in a focus group discussion through purposive sampling. The main objectives of this survey were to identify the understanding and acceptance levels of the seven smart city domains and respective strategies that are outlined in the Malaysian Smart City Framework. The results disclosed that the practitioners possessed divergent levels of understanding and acceptance in terms of smart city domains. The study participant practitioners accepted all understanding and acceptance objectives of smart economy, living, people, and governance domains (expert agreement 75–92% and threshold d value 0.123–0.188), but rejected all objectives for both smart environment and digital infrastructure domains (expert agreement 55–74% and threshold d value 0.150–0.212). Along with this, acceptance of smart mobility was also rejected (expert agreement 56% and threshold d value 0.245). The findings reveal that considering all opinions expressing dissensus is essential when building more inclusive smart city strategies. This study contributes to the smart city discourse as being one of the first in capturing professional practitioners’ understanding and acceptance on a national level smart city policy by applying the Delphi method in the smart city context. Most importantly, the study informs urban policymakers on how to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.

Assessment of Smart Home: Security and Privacy

Home automation is now extremely common in Internet of things services and devices with a range of assurances to improve health, lifestyle, and customer wellbeing. In terms of its success and apparent utility for humans, intelligent homes possess various safety concerns resulting from the diversified, vast-range, and nuanced nature of IoT. Previous studies have talked about security and privacy issues. However, we observe that they have not addressed the risk assessment of each smart home component and corresponding security objective along with additional factors that affect a smart home security posture. In this study, we have proposed a framework defining a standard level of security and then analyzing each component concerning it. There are so many vulnerabilities, but all cannot be assessed due to the heterogeneity of devices and their connection in a small network. IoT can support a wide range of technologies and programs in various domains, including smart cities and smart houses. For monitoring, data exchange, and other operations in the given service, IoT smart objects communicate with other elements such as proxies, mobile devices, and data collectors. While such components help solve various social issues and provide consumers with modern advanced services, their restricted computing capacities render them vulnerable to well-known protection and privacy risks.

Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science.

Inclusive Smart Cities: Beyond Voluntary Corporate Data Sharing

Smart cities mobilise technologically driven solutions toward urban governance and service delivery. The profitable opportunities and abundance of data made available by cities attract ICT corporations that possess the resources and knowledge to make cities smart. However, this often leads corporate actors to monopolise the data collected and generated. This poses risks for privacy and the ways in which personal data are used and commercialised. Existing work on business-to-government (B2G) data sharing and data collaboratives has explored the technical and organisational issues involved in corporate data sharing with public authorities. However, many studies remain focused on voluntary corporate data releases. This paper argues that the option of compelling companies to share data should be considered more attentively; it is one channel (among many) that has the potential to make cities more inclusive.

Is your smart city inclusive? Evaluating proposals from the U.S. Department of Transportation's Smart City Challenge

The concept of “Smart City” has been proposed by governments, the business community, advocacy groups, and research institutions as a means to solve common urban problems and improve the quality of life for citizens. Although a Smart City has the potential to change our cities for the better, it also may unintentionally reinforce existing inequalities. In particular, without appropriate strategies that support inclusion, persons with disabilities and seniors may experience social and digital exclusion in communities. This study explored current progress toward building an “Inclusive Smart City (ISC)” through the 2015 U.S. DOT Smart City Challenge. It examined the range and frequency of inclusive strategies that were proposed by cities in their applications and the differences between successful and unsuccessful proposals. After reviewing and analyzing documentation from both rounds, we conclude: (1) insufficient attention was given to these underrepresented population groups in the proposals; (2) more ISC strategies are needed to address these groups’ needs and guarantee their rights; (3) government policies to support ISCs are needed to insure that the implementation of Smart City ideas addresses the needs of these groups; and, (4) universal design practices could be used to address the needs of many underrepresented populations.

A State of the Art Survey of Machine Learning Algorithms for IoT Security

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

Smart Classroom Using Embedded System

A design is proposed using IR and LDR sensor for automation of lights and fans using ATMEGA 328P with Internet of Things for smart collage. Nowadays we’re having automation of every little electrical device in our homes and collage’s. Internet of Things is the concept of basically connecting any device with an on and off switch to the internet. EMBEDDED SYSTEM is more than smart homes and collage’s and connected appliances; however, it scales up to include smart cities with connected sensors. Using IR sensors, the lights will automatically turn on and off according to the intensity of light. Temperature sensors will detect the room temperature and turn on and off fans.

A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky–Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and strong nonlinear features in the network traffic due to continuous expansion of network scale and fast emergence of new services. Current prediction methods usually have oversimplified theoretical assumptions, need significant time and memory, or suffer problems of gradient disappearance or early convergence. Thus, they fail to effectively capture the nonlinear characteristics of large-scale network sequences. This work proposes a hybrid prediction method named SG and TCN-based LSTM (ST-LSTM), which integrates the merits of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term memory (LSTM), serving as smoothing time series, capturing short-term local features, and capturing long-term dependence, respectively. Experimental results based on the real-life dataset demonstrate that it achieves better prediction accuracy than its state-of-the-art peers, including the TCN and the LSTM. It can be readily implemented and deployed in many real-life industrial areas including smart city, edge computing, cloud computing, and data centers.

Monitoring and evaluation framework for inclusive smart cities in India

ABSTRACT Urban investments in India over the past three decades have helped its cities advance in terms of improved infrastructure and better economic standards. However, the cities are simultaneously facing challenges of polarised and unequally distributed development benefits. This paper advances the inclusive cities’ agenda beyond the constructs of “pro-poor development” and develops a multidimensional evaluation and monitoring framework. Through the review and analysis of 20 smart cities in India, the paper presents the gaps and patterns of relationships within the critical factors of inclusion. The need to develop alternate development models that shall promote socio-economic transformation in smart cities in India is emphasised through this paper.

On the Application of Big Data and its Intelligent Processing Technology in Internet of Things Industry

Big data and the Internet of Things are developing at a rapid pace, bringing great opportunities for the application of various technology fields, including smart cities, smart transportation, smart grid and telemedicine consultation. The wide application of the Internet of Things makes big data analysis challenging, as data is collected and processed by different sensors [1]. Based on this, this paper explores the application of big data and its intelligent processing technology in the Internet of Things industry.

Towards a low cost fully integrated IGZO TFT NO2 detection and quantification: A solution-processed approach

Abstract Semiconducting metal oxide gas sensors that are inexpensive, room temperature operable are imperative for toxic gas and air pollution monitoring. The heating or continuous light activation requirements are unavoidable for these types of gas sensors. This paper reports for the first time a room temperature operable solution-processed In-Ga-Zn-O thin-film transistor (TFT) gas sensor to detect NO2 without continuous light activation. The TFT sensor exhibits promising sensing and selectivity towards NO2. The devices are re-generable and require light activation only to recover after exposure to the gas. Kelvin probe force microscopy characterization studies are performed to understand in detail the sensing mechanism of the solution-processed TFT device. A novel solution-processed TFT based gas-sensitive digital indicators that yield digital output quantifying the NO2 concentration are demonstrated. One of the integrated gas-sensitive digital indicators is developed using only two InGaZnO TFTs incorporating a signal conditioning circuit and analog to digital converter. Furthermore, it paves the way for portable, compact, and inexpensive systems for various gas sensing applications, including smart cities and sustainable ecosystems.

Estrategia en ciudades inteligentes e inclusión social del adulto mayor

Smart cities are increasingly necessary in today's society, however the social inclusion of the elderly within these seems a forgotten issue that needs to be considered for the construction of an inclusive society. The objective of this research is to explore the social inclusion of the elderly in smart cities, as well as their evolution, scientometric characteristics and trends by means of a bibliometric analysis. The work analyzes bibliometric metadata of 244 Scopus documents, published from 2010 to 2020 using Software R, Bibliometrix and Biblioshiny. The conclusions indicate that a growing interest is maintained over time, and this is perceived mainly in conferences; in addition, thematic trends reveal elements such as the internet of things, design and implementation, and e-commerce among others. Likewise, the emergence of the term inclusive smart city was evidenced, which considers the integration into society of vulnerable groups. It is expected that this work will serve as a reference for an inclusive design of smart cities.

Public Trust and Political Legitimacy in the Smart City: A Reckoning for Technocracy

The 2020 introduction by China’s central government of a national security law (NSL) in Hong Kong marked a watershed moment in the social and political history of the semiautonomous city. The law emerged after months of street protests that reflected declining public trust in Hong Kong’s government. Against this turbulent backdrop, Hong Kong’s policy projects moved forward, including smart city development. This article explores public trust in and political legitimacy of Hong Kong’s smart cities endeavors in the period leading up to the introduction of the NSL. At a theoretical level, the smart cities phenomenon invites critical reflection about tensions between technocracy and democracy, but this topic remains largely unexploited by empirical literature. Using survey data from 1,017 residents, this study identifies confidence in the benefits of smart cities but lesser trust in privacy and security and lesser satisfaction with participation opportunities in related policymaking. Probing these dynamics, the study finds that trust in smart city mechanics and governance associate positively with support for smart cities, controlling for ideology and issue awareness. Illuminating a theoretical and practical puzzle, these findings contribute empirically to discussions about the political legitimacy of scientific, technological, and technocratic undertakings in the public sector.