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25 pages, 2143 KiB

Open AccessArticle

ZARATAMAP: Noise Characterization in the Scope of a Smart City through a Low Cost and Mobile Electronic Embedded System

by Unai Hernandez-Jayo and Amaia Goñi

Sensors 2021, 21(5), 1707; https://doi.org/10.3390/s21051707 - 2 Mar 2021

Cited by 7 | Viewed by 2698

Abstract

Like other sources of pollution, noise is considered to be one of the main concerns of citizens, due to its invisibility and the potential harm it can cause. Noise pollution could be considered as one of the biggest quality-of-life concerns for urban residents [...] Read more.

Like other sources of pollution, noise is considered to be one of the main concerns of citizens, due to its invisibility and the potential harm it can cause. Noise pollution could be considered as one of the biggest quality-of-life concerns for urban residents in big cities, mainly due to the high levels of noise to which they may be exposed. Such levels have proven effects on health, such as: sleep disruption, hypertension, heart disease, and hearing loss. In a scenario where the number of people concentrated in cities is increasing, tools are needed to quantify, monitor, characterize, and quantify noise levels. This paper presents the ZARATAMAP project, which combines machine learning techniques with a geo-sensing application so that the authorities can have as much information as possible, using a low-cost embedded and mobile node, that is easy to deploy, develop, and use. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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26 pages, 1558 KiB

Open AccessArticle

Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values

by Zeinab Farahmandpour, Mehdi Seyedmahmoudian, Alex Stojcevski, Irene Moser and Jean-Guy Schneider

Sensors 2020, 20(19), 5664; https://doi.org/10.3390/s20195664 - 3 Oct 2020

Cited by 6 | Viewed by 2330

Abstract

Continuous delivery has gained increased popularity in industry as a development approach to develop, test, and deploy enhancements to software components in short development cycles. In order for continuous delivery to be effectively adopted, the services that a component depends upon must be [...] Read more.

Continuous delivery has gained increased popularity in industry as a development approach to develop, test, and deploy enhancements to software components in short development cycles. In order for continuous delivery to be effectively adopted, the services that a component depends upon must be readily available to software engineers in order to systematically apply quality assurance techniques. However, this may not always be possible as (i) these requisite services may have limited access and (ii) defects that are introduced in a component under development may cause ripple effects in real deployment environments. Service virtualisation (SV) has been introduced as an approach to address these challenges, but existing approaches to SV still fall short of delivering the required accuracy and/or ease-of-use to virtualise services for adoption in continuous delivery. In this work, we propose a novel machine learning based approach to predict numeric fields in virtualised responses, extending existing research that has provided a way to produce values for categorical fields. The SV approach introduced here uses machine learning techniques to derive values of numeric fields that are based on a variable number of pertinent historic messages. Our empirical evaluation demonstrates that the Cognitive SV approach can produce responses with the appropriate fields and accurately predict values of numeric fields across three data sets, some of them based on stateful protocols. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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21 pages, 5702 KiB

Open AccessArticle

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

by Zi-Qi Huang, Ying-Chih Chen and Chih-Yu Wen

Sensors 2020, 20(18), 5173; https://doi.org/10.3390/s20185173 - 10 Sep 2020

Cited by 26 | Viewed by 9903

Abstract

Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses [...] Read more.

Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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24 pages, 3176 KiB

Open AccessArticle

Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller

by Taimoor Ahmad Khan, Kalim Ullah, Ghulam Hafeez, Imran Khan, Azfar Khalid, Zeeshan Shafiq, Muhammad Usman and Abdul Baseer Qazi

Sensors 2020, 20(16), 4376; https://doi.org/10.3390/s20164376 - 5 Aug 2020

Cited by 10 | Viewed by 3807

Abstract

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet [...] Read more.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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24 pages, 917 KiB

Open AccessArticle

Edge Computing and Blockchain for Quick Fake News Detection in IoV

by Yonggang Xiao, Yanbing Liu and Tun Li

Sensors 2020, 20(16), 4360; https://doi.org/10.3390/s20164360 - 5 Aug 2020

Cited by 32 | Viewed by 4555

Abstract

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News [...] Read more.

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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24 pages, 1274 KiB

Open AccessArticle

A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities

by Tariq Ali, Muhammad Irfan, Ahmad Shaf, Abdullah Saeed Alwadie, Ahthasham Sajid, Muhammad Awais and Muhammad Aamir

Sensors 2020, 20(15), 4309; https://doi.org/10.3390/s20154309 - 2 Aug 2020

Cited by 20 | Viewed by 4502

Abstract

Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic [...] Read more.

Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic medium is used in underwater communication and radio frequency is mostly used for wireless sensor networks to make communication more reliable. Therefore, some challenging tasks still exist in I-UWSN, i.e., selection of multiple nodes’ reliable paths towards the sink nodes; and efficient topology of the network. In this research, the novel routing protocol, namely Time Based Reliable Link (TBRL), for dynamic topology is proposed to support smart city. TBRL works in three phases. In the first phase, it discovers the topology of each node in network area using a topology discovery algorithm. In the second phase, the reliability of each established link has been determined while using two nodes reliable model for a smart environment. This reliability model reduces the chances of horizontal and higher depth level communication between nodes and selects next reliable forwarders. In the third phase, all paths are examined and the most reliable path is selected to send data packets. TBRL is simulated with the help of a network simulator tool (NS-2 AquaSim). The TBRL is compared with other well known routing protocols, i.e., Depth Based Routing (DBR) and Reliable Energy-efficient Routing Protocol (R-ER

P^{2}

R), to check the performance in terms of end to end delay, packet delivery ratio, and energy consumption of a network. Furthermore, the reliability of TBRL is compared with 2H-ACK and 3H-RM. The simulation results proved that TBRL performs approximately 15% better as compared to DBR and 10% better as compared to R-ER

P^{2}

R in terms of aforementioned performance metrics. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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29 pages, 1635 KiB

Open AccessArticle

An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities

by Nighat Usman, Omar Alfandi, Saeeda Usman, Asad Masood Khattak, Muhammad Awais, Bashir Hayat and Ahthasham Sajid

Sensors 2020, 20(15), 4116; https://doi.org/10.3390/s20154116 - 24 Jul 2020

Cited by 18 | Viewed by 3586

Abstract

Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from [...] Read more.

Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used efficiently to improve the lifetime of a network. In this paper, to support smart city vision, a terrestrial based “Away Cluster Head with Adaptive Clustering Habit” (ACH)

^{2}

is examined in the specified three dimensional (3-D) region inside the water. Three different cases are considered, which are: single sink at the water surface, multiple sinks at water surface,, and sinks at both water surface and inside water. “Underwater (ACH)

^{2}

” (U-(ACH)

^{2}

) is evaluated in each case. We have used depth in our proposed U-(ACH)

^{2}

to examine the performance of (ACH)

^{2}

in the ocean environment. Moreover, a comparative analysis is performed with state of the art routing protocols, including: Depth-based Routing (DBR) and Energy Efficient Depth-based Routing (EEDBR) protocol. Among all of the scenarios followed by case 1 and case 3, the number of packets sent and received at sink node are maximum using DEEC-(ACH)

^{2}

protocol. The packets drop ratio using TEEN-(ACH)

^{2}

protocol is less when compared to other algorithms in all scenarios. Whereas, for dead nodes DEEC-(ACH)

^{2}

, LEACH-(ACH)

^{2}

, and SEP-(ACH)

^{2}

protocols’ performance is different for every considered scenario. The simulation results shows that the proposed protocols outperform the existing ones. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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41 pages, 3095 KiB

Open AccessArticle

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid

by Ghulam Hafeez, Zahid Wadud, Imran Ullah Khan, Imran Khan, Zeeshan Shafiq, Muhammad Usman and Mohammad Usman Ali Khan

Sensors 2020, 20(11), 3155; https://doi.org/10.3390/s20113155 - 2 Jun 2020

Cited by 94 | Viewed by 9748

Abstract

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities [...] Read more.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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19 pages, 4854 KiB

Open AccessArticle

Moving towards IoT Based Digital Communication: An Efficient Utilization of Power Spectrum Density for Smart Cities

by Tariq Ali, Abdullah S. Alwadie, Abdul Rasheed Rizwan, Ahthasham Sajid, Muhammad Irfan and Muhammad Awais

Sensors 2020, 20(10), 2856; https://doi.org/10.3390/s20102856 - 18 May 2020

Cited by 3 | Viewed by 5408

Abstract

The future of the Internet of Things (IoT) is interlinked with digital communication in smart cities. The digital signal power spectrum of smart IoT devices is greatly needed to provide communication support. The line codes play a significant role in data bit transmission [...] Read more.

The future of the Internet of Things (IoT) is interlinked with digital communication in smart cities. The digital signal power spectrum of smart IoT devices is greatly needed to provide communication support. The line codes play a significant role in data bit transmission in digital communication. The existing line-coding techniques are designed for traditional computing network technology and power spectrum density to translate data bits into a signal using various line code waveforms. The existing line-code techniques have multiple kinds of issues, such as the utilization of bandwidth, connection synchronization (CS), the direct current (DC) component, and power spectrum density (PSD). These highlighted issues are not adequate in IoT devices in smart cities due to their small size. However, there is a need to design an effective line-code method to deal with these issues in digital IoT-based communication for smart technologies, which enables smart services for smart cities. In this paper, the Shadow Encoding Scheme (SES) is proposed to transmit data bits efficiently by using a physical waveform in the smart cities’ ecosystem. SES provides a reliable transmission over the physical medium without using extra bandwidth and with ideal PSD. In it, the shadow copy of the repeating bitstream is forwarded, rather than repeating the actual stream again and again. The PSD is calculated with the help of mathematical equations to validate SES. MATLAB simulator is used to simulate SES and compared with other well-known digital line-code techniques. The bit error rate is also compared between SES and the chirp spread spectrum (CSS) for the specific data frames. The coordinates of the PSD graph are also shown in tabular form, which shows a vivid picture of the working conditions of various line codes. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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24 pages, 28307 KiB

Open AccessArticle

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

by Ihsane Gryech, Yassine Ben-Aboud, Bassma Guermah, Nada Sbihi, Mounir Ghogho and Abdellatif Kobbane

Sensors 2020, 20(4), 998; https://doi.org/10.3390/s20040998 - 13 Feb 2020

Cited by 52 | Viewed by 9680

Abstract

MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is [...] Read more.

MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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Review

Jump to: Research

28 pages, 2222 KiB

Open AccessReview

Advances and Trends in Real Time Visual Crowd Analysis

by Khalil Khan, Waleed Albattah, Rehan Ullah Khan, Ali Mustafa Qamar and Durre Nayab

Sensors 2020, 20(18), 5073; https://doi.org/10.3390/s20185073 - 7 Sep 2020

Cited by 24 | Viewed by 7492

Abstract

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large [...] Read more.

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community. Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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33 pages, 5158 KiB

Open AccessReview

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

by Naveed Ilyas, Ahsan Shahzad and Kiseon Kim

Sensors 2020, 20(1), 43; https://doi.org/10.3390/s20010043 - 19 Dec 2019

Cited by 81 | Viewed by 12614

Abstract

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the [...] Read more.

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT). Full article

(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)

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