scholarly journals Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 784
Author(s):  
Yazan Qarout ◽  
Yordan P. Raykov ◽  
Max A. Little
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Smart Cities ◽  
Low Cost ◽  
Deep Understanding ◽  
Time Of Day ◽  
Sensor Data ◽  
Observation Data ◽  
Transition Matrices ◽  
Time Dynamics

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

scholarly journals Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

2021 ◽  
Vol 6 (1) ◽  
pp. 35
Author(s):  
Yazan Qarout ◽  
Yordan P. Raykov ◽  
Max A. Little
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Smart Cities ◽  
Low Cost ◽  
Deep Understanding ◽  
Time Of Day ◽  
Sensor Data ◽  
Observation Data ◽  
Transition Matrices ◽  
Time Dynamics

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport, and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative which enables deep understanding of population behaviour, such as the Global Positioning System (GPS) data. However, the automated analysis of such low-dimensional sensor data requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day, or the difference between weekend/weekday trends. We propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations, all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM), is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

scholarly journals Positioning Evaluation of Single and Dual-Frequency Low-Cost GNSS Receivers Signals Using PPP and Static Relative Methods in Urban Areas

2021 ◽  
Vol 11 (22) ◽  
pp. 10642
Author(s):  
Rosendo Romero-Andrade ◽  
Manuel E. Trejo-Soto ◽  
Alejandro Vega-Ayala ◽  
Daniel Hernández-Andrade ◽  
Jesús R. Vázquez-Ontiveros ◽  
...  
Keyword(s):  
Urban Areas ◽  
Precise Point Positioning ◽  
Low Cost ◽  
Observation Data ◽  
Dual Frequency ◽  
Positional Accuracy ◽  
Gnss Receivers ◽  
Point Positioning ◽  
Error Circle ◽  
Vertical Positioning

A positional accuracy obtained by the Precise Point Positioning and static relative methods was compared and analyzed. Test data was collected using low-cost GNSS receivers of single- and dual-frequency in urban areas. The data was analyzed for quality using the TEQC program to determine the degree of affectation of the signal in the urban area. Low-cost GNSS receivers were found to be sensitive to the multipath effect, which impacts positioning. The horizontal and vertical accuracy was evaluated with respect to Mexican regulations for the GNSS establishment criteria. Probable Error Circle (CEP) and Vertical Positioning Accuracy (EPV) were performed on low cost GNSS receiver observation data. The results show that low-cost dual-frequency GNSS receivers can be used in urban areas. The precision was obtained in the order of 0.013 m in the static relative method. The results obtained are comparable to a geodetic receiver in a geodetic baseline of <20 km. The study does not recommend using single and dual frequencies low cost GNSS receivers based on results obtained by the Precise Point Positioning (PPP) method in urban areas. The inclusion of the GGM10 model reduces the vertical precision obtained by using low cost GNSS receivers in both methods, conforming to the regulations only in the horizontal component.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Kalliopi Kyriakou ◽  
Bernd Resch
Keyword(s):  
Spatial Analysis ◽  
Urban Areas ◽  
Hot Spot ◽  
Low Cost ◽  
Field Studies ◽  
Physiological Signals ◽  
Sensor Data ◽  
Skin Response ◽  
Density Analysis ◽  
Physiological Sensors

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

Smart Cities and Digital Governance

2020 ◽  
pp. 87-106
Author(s):  
Mamoona Humayun ◽  
N. Z. Jhanjhi ◽  
Malak Z. Alamri ◽  
Azeem Khan
Keyword(s):  
Smart City ◽  
Urban Areas ◽  
Good Governance ◽  
Smart Cities ◽  
Low Cost ◽  
Developed Countries ◽  
Digital Governance ◽  
Smart Governance ◽  
Intelligent Security ◽  
The City

With the ubiquitous low-cost sensor devices and widespread use of IoT, the paradigm is shifted from urban areas towards a smart city. A smart city is an urban area that uses IoT technologies to collect data and manage resources efficiently. The vision is to improve the capabilities and to solve the citizens' problems (e.g., energy consumption, transportation, recycling, intelligent security, etc.) in an efficient way. A smart city is a multidimensional term including a smart economy, smart mobility, smart living, smart environment, smart people, and smart governance. Although the concept of a smart city is increasing and currently there exist many such cities in many developed countries, one of the key challenges faced by these cities is good governance. Smart cities need smart governance to run the city in a smarter way, and effective digital governance is a solution to this end. Digital governance refers to the use of digital technology in government practices.

New Low-Cost Transport Planning Method for Small and Medium-Sized Cities in Developing Countries

2020 ◽  
Vol 2674 (9) ◽  
pp. 922-932
Author(s):  
Michael J. Saunders ◽  
Lasha Nakashidze ◽  
Aleksei Lugovoi
Keyword(s):  
Developing Countries ◽  
Public Transport ◽  
Urban Areas ◽  
City Planning ◽  
Low Cost ◽  
Sub Saharan Africa ◽  
Advanced Degree ◽  
Transport Planning ◽  
Planning Methods ◽  
Sub Saharan

Traditional transport planning methods are costly and require an advanced degree of understanding not only from the involved transport planning professionals, but also the politicians who must approve the resulting outcomes and transport interventions proposed that are based on these traditional methods. A different approach is proposed for small and medium-sized cities in developing countries that have less technical expertise and fewer financial resources to improve their public transport situation. This approach was trialed in a medium-sized city in West-Asia (Batumi, Georgia) and also in Central Asia, in a larger city (Bishkek, Kyrgyzstan). The planning interventions suggested in the medium-sized city were validated by an independent consultant at the request of Batumi City planning agency, using traditional transport planning methods, which shows promise for the new low-cost method proposed. With additional validation and research, it may be possible to expand and apply this method to South Asia, sub-Saharan Africa, and any other area of the world suffering from similar transport planning constraints to these developing regions. If successful, these planning methods could rapidly transform such cities and urban areas to become less carbon intensive and concurrently more efficient and comfortable for public transport users.

scholarly journals Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3079
Author(s):  
André Glória ◽  
João Cardoso ◽  
Pedro Sebastião
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
New Technologies ◽  
Irrigation System ◽  
Low Cost ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Sensors And Actuators

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

scholarly journals Measure particulate matter by yourself: data-quality monitoring in a citizen science project

2019 ◽  
Vol 8 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Aboubakr Benabbas ◽  
Martin Geißelbrecht ◽  
Gabriel Martin Nikol ◽  
Lukas Mahr ◽  
Daniel Nähr ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Data ◽  
And Control ◽  
Future Work ◽  
The Impact

Abstract. The concern about air quality in urban areas and the impact of particulate matter (PM) on public health is turning into a big debate. A good solution to sensitize people to this issue is to involve them in the process of air quality monitoring. This paper presents contributions in the field of PM measurements using low-cost sensors. We show how a low-cost PM sensor can be extended to transfer data not only over Wi-Fi but also over the LoRa protocol. Then, we identify some of the correlations existing in the data through data analysis. Afterwards, we show how semantic technologies can help model and control sensor data quality in an increasing PM sensor network. We finally wrap up with a conclusion and plans for future work.

scholarly journals THREE-DIMENSIONAL DIGITIZATION OF ENVIRONMENTS AND BUILDINGS FOR SMART CITY APPLICATIONS

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 65-71
Author(s):  
C. Altuntas
Keyword(s):  
Spatial Data ◽  
Smart City ◽  
Urban Areas ◽  
Spatial Information ◽  
Smart Cities ◽  
Low Cost ◽  
Measurement Techniques ◽  
Night Time ◽  
Data Infrastructure ◽  
Cloud Data

Abstract. The smart cities that promise a sustainable future cannot be thought of independently from the spatial information infrastructure. It is very important to keep the spatial data infrastructure up-to-date for guidance and information in smart city applications (SCA). Easy and low-cost acquisition is an important factor in updating spatial data. Today, there are many measurement techniques to collecting 3-D spatial data of urban areas and land topography. On the other hand, indoor measurement and 3-D modelling techniques are used in the creation of building information modelling (BIM). In this study, measurement techniques that provide 3-D point cloud data to SCA are examined. Consequently, image based photogrammetry and dense matching methods enable low cost measurement than LiDAR based active measurement. The active 3-D measurement techniques have high accuracy especially for mid and long ranges. The LiDAR, that can be applied at day or night time, offer more opportunity to performing SCA like autonomous vehicle and robotic navigations. Nevertheless, LiDAR can only capture structure, not texture, and therefore has limits to the types of data that it can capture. The LiDAR and image based methods are complement to each other in 3-D reality capture. The 3-D measurement techniques are exploited according to SCA as alone or together.

scholarly journals Toward Near Real-Time Kinematics Differential Correction: In View of Geometrically Augmented Sensor Data for Mobile Microclimate Monitoring

2020 ◽  
Vol 2 (1) ◽  
pp. 61
Author(s):  
Stefano Tondini ◽  
Farshad Hasanabadi ◽  
Roberto Monsorno ◽  
Antonio Novelli
Keyword(s):  
Urban Communities ◽  
Urban Areas ◽  
Global Climate ◽  
Low Cost ◽  
Life Quality ◽  
Environmental Parameters ◽  
Mitigation Strategies ◽  
Added Value ◽  
Sensor Data ◽  
Urban Context

In the scenario of massive urbanization and global climate change, the acquisition of microclimatic data in urban areas plays a key role in responsive adaptation and mitigation strategies. The enrichment of kinematic sensor data with precise, high-frequency and robust positioning directly relates to the possibility of creating added-value services devoted to improving the life-quality of urban communities. This work presents a low-cost cloud-connected mobile monitoring platform for multiple environmental parameters and their spatial variation in the urban context.

Sign in / Sign up

Export Citation Format

Share Document