Sensor Infrastructure Research Articles

Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia

ABSTRACTTraffic congestion represents a significant urban challenge, with notable implications for public health and environmental well‐being. Consequently, urban decision‐makers prioritize the mitigation of congestion. This study delves into the efficacy of harnessing extensive data on urban traffic dynamics, coupled with comprehensive knowledge of road networks, to enable Artificial Intelligence (AI) in forecasting traffic flux well in advance. Such forecasts hold promise for informing emission reduction measures, particularly those aligned with Low Emission Zone policies. The investigation centers on Valencia, leveraging its robust traffic sensor infrastructure, one of the most densely deployed worldwide, encompassing approximately 3500 sensors strategically positioned across the city. Employing historical data spanning 2016 and 2017, we undertake the task of training and characterizing a Long Short‐Term Memory (LSTM) Neural Network for the prediction of temporal traffic patterns. Our findings demonstrate the LSTM's efficacy in real‐time forecasting of traffic flow evolution, facilitated by its ability to discern salient patterns within the dataset.

Usability and User Experience Evaluation in Intelligent Environments: A Review and Reappraisal

Intelligent environments are rapidly gaining ground, propelled by a rich sensor infrastructure, the Internet of Things, sophisticated reasoning capabilities, and Artificial Intelligence. In this complex technological landscape, crafting usable intelligent environments and assessing the user experience (UX) demands a thorough understanding of the concepts involved and the parameters that need to be studied. This paper carries out a review of usability and UX evaluation methods and frameworks, elaborating on fundamental concepts and presenting in detail approaches and methods reported in the literature. It additionally examines evaluation approaches in adaptive and ubiquitous computing systems, which are closely associated with intelligent environments, and presents UX challenges and evaluation frameworks in intelligent environments. Finally, the findings are synthesized and consolidated to produce a comprehensive overview of the field and the challenges that lie ahead.

Critical infrastructure monitoring in CBRNe scenarios: a reliable and robust communication network for distributed multimodal sensors

The malicious misuse of CBRNe agents can inflict extensive damage to critical infrastructure and terrorize public society. The effects of such attacks can range from financial or structural drawbacks to significant casualties. Recent terror attacks in Cucuta (2021) or Kabul (2021) demonstrate the need to protect infrastructures such as airports, power plants, and transportation infrastructure. In the face of these modern-day threats, the need for a reliable and effective monitoring system for critical infrastructure has become increasingly important. In a first contribution to the long-term development, we present an initial version of a concept that requires the implementation paradigm of diverse redundancy and secure communication using MQTTS relying on the included cipher suite. An initial set of node types is defined and assigned requirements for implementation. The software tools architecture we propose based on these requirements is designed to support network operators and developers by providing standard features for network management and sensor node implementation. This includes the definition of a secure semi-automatic onboarding process for new sensor nodes, which is presented in detail. We strive for independency from specific hardware platforms, software frameworks, and network technologies to enable an open standard for communication within a critical infrastructure sensor network and also between such networks in the future.

FETR: Feature Transformer for vehicle-infrastructure cooperative 3D object detection

3D object detection plays a crucial role in the perception system of autonomous vehicles, however, the vehicle’s field of view is restricted due to obstructions from nearby vehicles and buildings. Vehicle-infrastructure cooperation can compensate for the issue of visibility, but due to discrepancies in timestamps between vehicle and infrastructure sensors as well as data transmission delays, there is typically a time asynchrony between vehicle and infrastructure data. Therefore, Feature Transformer (FETR) has been introduced, which is a vehicle-infrastructure cooperative 3D object detection model utilizing Transformer as a Feature Predictor. The Transformer Predictor is capable of predicting features of future frame based on the current frame features, efficiently addressing the problem of time asynchrony. Additionally, to enhance the precision of 3D object detection, we have introduced a plug-and-play module named Mask Feature Enhancement (MFE), MFE employs a mask to amplify the features in the object region while simultaneously diminishing the features of the surrounding environment, enlarging the difference between object features and environmental features, thereby improving the detection effect. Experimental results show that FETR attains a 68.15 BEV-mAP (IoU=0.5) on the DAIR-V2X dataset, with a 200ms latency, and the data transmission is merely 6.0×104 bytes, constituting just 4.2% of the original point cloud data, outperforming current vehicle-infrastructure cooperative models in terms of both precision and data transmission.

Object Reconstruction and Localization in Indoor Environments Using Infrastructure Sensor Node

Object Reconstruction and Localization in Indoor Environments Using Infrastructure Sensor Node

Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety

Cooperative perception that integrates sensing capabilities from both infrastructure and vehicle perception sensors can greatly benefit the transportation system with respect to safety and data acquisition. In this study, we conduct a preliminary evaluation of such a system by integrating a portable lidar-based infrastructure detection system (namely, Traffic Scanner [TScan]) with a Society of Automotive Engineers (SAE) Level 4 connected and automated vehicle (CAV). Vehicle-to-everything (V2X) communication devices are installed on both the TScan and the CAV to enable real-time message transmission of detection results in the form of SAE J2735 basic safety messages. We validate the concept using a case study, which aims at improving CAV situation awareness and protecting vulnerable road user (VRU) safety. Field testing results demonstrate the safety benefits of cooperative perception from infrastructure sensors in detecting occluded VRUs and helping CAVs to plan safer (i.e., higher post-encroachment time) and smoother (i.e., lower deceleration rates) trajectories.

Multi-Domain Smart Safety Helmet

Motorcycle accidents and head injuries are critical concerns globally, especially where helmet non-compliance is prevalent. To address this, a multi-domain smart safety helmet is proposed for rider safety and accident prevention through advanced sensor technology. This smart helmet integrates sensors like an MQ-3 alcohol sensor and helmet detection sensors to ensure safety conditions before the motorcycle engine starts. If alcohol levels exceed a threshold, the ignition system disables, preventing intoxicated riding. Helmet detection promotes helmet use, reducing head injury risk. The scalable sensor infrastructure enables multi-domain applications beyond motorcycles. This helmet can integrate into industries like coal mining and firefighting. In coal mining, it monitors environmental conditions and worker vital signs. In firefighting, it detects hazardous gases and monitors firefighter status. During motorcycle operation, the helmet continuously monitors critical parameters—speed, tilt, and environment—providing immediate feedback on unsafe behaviours. In accidents, the helmet's accelerometer detects impacts, activating GPS to pinpoint the location and GSM to alert emergency contacts. This smart helmet aims to enhance motorcycle safety, prevent accidents, and expedite emergency responses. It represents progress towards reducing motorcycle-related injuries and fatalities, with adaptable features for broader safety applications across industries and domains.

Making events: How anticipatory infrastructures produce shared temporalities

Anticipatory infrastructures assemble sensors that are ready to detect, networks primed to share data, scientists prepared to confirm events, and news organizations poised to tell stories. This article explains how public time is articulated through sensor-mediated communications by examining two anticipatory infrastructures. Each infrastructure uses similar earthquake data to detect, report on, and convene material publics around earthquakes in Southern California. They are integral to structuring rhythms, coordinating syncronizations, setting deadlines, and making events timely, meaningful, and actionable, yet their governance lives in no one place. Instead, they emerge from an assemblage of sensors, networks, devices, algorithms, people, data, organizations, professional practices, and normative theories of the public. By comparing two different anticipatory infrastructures, we show how imagined publics, forms of journalistic storytelling, representations of earthquake events, and system maintenance can convene different public temporalities. We identify four dynamics involved in making these variable temporalities in material publics: how human-machine relations organize time, how professional norms of timeliness collide, how publics are anticipated by infrastructures, and how sensor infrastructures are maintained or decay over time.

Anomaly Detection in Data Center IT & Physical Infrastructure

Anomaly detection in data center IT and physical infrastructure is challenging due to the amount of heterogeneous data to be analyzed. Defining a solution that early identifies unexpected anomalies is particularly important to prevent data losses, breakdown of the system, and any other event considered to be critical for the activity of the data center. In the context of the INFN CNAF data center, one of the WLCG Tier-1s, we have performed a study based on monitored cooling, electrical, and IT hardware and software metrics to identify anomalies. In the present work, we aim to explore statistical approaches and machine learning solutions in the anomaly detection field for time series numerical metrics related to IT and physical infrastructure sensors. With the usage of statistical Z-score and percentile approaches and clustering DBSCAN technique, we have been able to group and identify anomalous events. Using the presented approach, different relevance can be attributed to the likely anomalies.

Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%.

A Protocol for Reliable Real-Time Wireless Communication of Large Data Samples

In this paper, we present a middleware protocol, that transmits larger-than-a-frame data samples within application real-time and reliability requirements over a lossy wireless channel. The protocol efficiently deploys an intelligent retransmission control that exploits the extended deadline requirements of a sample for frame-level scheduling. The transmission of such samples is placed in the context of the necessary integration of such external sensor data into the decision-making process of an autonomous vehicle. Therefore, we provide parameterization and access rules for communication links in resource-managed scenarios and lay out how to integrate the protocol into the popular DDS middleware. The performance of the parameterized links to satisfy their reliability requirements is studied and compared to error protection protocols in the current software stack. We base the evaluation on an OMNeT++ simulation, whereby an established wireless error model is used. The use case is placed in the context of collaborative sensing in a valet parking environment, where external infrastructure sensor data augments the autonomous vehicle's data processing chain via a lossy wireless link. Thereby, the use case adheres to the underlying safety requirements. Results show that if the protocol's awareness of the application requirements is used for parameterization, guarantees for reliability can be provided that outperform existing solutions.

FEMTOSAT-Based Air Quality Monitoring: Leveraging Satellite Data and LoRa Communication for Improved AQI Predictions

The exponential growth of the global population has given rise to an alarming surge in air pollution levels, casting profound repercussions on economies, ecosystems, and human well-being. The predominant method for assessing air quality involves the deployment of sensors atop buildings at regular intervals. However, this approach faces significant drawbacks, including heightened energy consumption associated with each building's sensor infrastructure and its inapplicability in sparsely populated rural areas. Addressing these limitations, the utilization of FEMTOSAT technology emerges as a solution, capitalizing on satellite data for autonomous air pollution monitoring, analysis, and mitigation. Amidst the evolving scientific landscape, the integration of Low Power Wide Area Network (LoRa) technology assumes a pivotal role within the Internet of Things (IoT), facilitating long-range data communication with minimal power consumption. In this context, LoRa communication serves as the conduit for transmitting and receiving data from satellites via RF signals. The satellite-derived environmental data, thus collected, serves as the foundation for computing the Air Quality Index (AQI) at specific locations, a critical metric that informs us about air quality conditions, whether pristine or contaminated. The AQI computation factors in various pollutants, including NO2, CO, O3, PM2.5, SO2, and PM10, all of which significantly influence air quality. This study employs a range of machine learning (ML) techniques, including time series analysis, linear regression, Support Vector Machines (SVM), and logistic regression, to predict and forecast AQI values. These models amalgamate AQI data from diverse sources, yielding robust and dependable AQI prediction models. Notably, modern sensor technology simplifies and enhances data collection accuracy. In the realm of environmental data analysis, only ML algorithms can grapple with the complexity of processing vast datasets to generate precise and trustworthy predictions. The incorporation of integrated sensors as payload in the Femto Sat mission epitomizes the mission's objectives. This system is characterized by its cost-effectiveness, lightweight design, durability, redundancy, and user-friendly interface, requiring minimal power consumption for operation.

Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment

Infrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.

An Object Association Matching Method Based on V2I System

Vehicle-to-infrastructure (V2I) is one of the effective ways to solve the problem of intelligent connected vehicle perception, and the core is to fuse the information sensed by vehicle sensors with that sensed by infrastructure sensors. However, accurately matching the objects detected by the vehicle with multiple objects detected by the infrastructure remains a challenge. This paper presents an object association matching method to fuse the object information from vehicle sensors and roadside sensors, enabling the matching and fusion of multiple target information. The proposed object association matching algorithm consists of three steps. First, the deployment method for vehicle sensors and roadside sensors is designed. Then, the laser point cloud data from the roadside sensors are processed using the DBSCAN algorithm to extract the object information on the road. Finally, an improved single-pass algorithm for object association matching is proposed to achieve the matched target by setting a change threshold for selection. To validate the effectiveness and feasibility of the proposed method, real-vehicle experiments are conducted. Furthermore, the improved single-pass algorithm is compared with the classical Hungarian algorithm, Kuhn–Munkres (KM) algorithm, and nearest neighbor (NN) algorithm. The experimental results demonstrate that the improved single-pass algorithm achieves a target trajectory matching accuracy of 0.937, which is 6.60%, 1.85%, and 2.07% higher than the above-mentioned algorithms, respectively. In addition, this paper investigates the curvature of the target vehicle trajectory data after fusing vehicle sensing information and roadside sensing information. The curvature mean, curvature variance, and curvature standard deviation are analyzed. The experimental results illustrate that the fused target information is more accurate and effective. The method proposed in this study contributes to the advancement of the theoretical system of V2I cooperative perception and provides theoretical support for the development of intelligent connected vehicles.

Analysis of Cyber Security Aspects of Data Transmission in Large-Scale Networks Based on the LoRaWAN Protocol Intended for Monitoring Critical Infrastructure Sensors

Cyber security is nowadays synonymous with the reliability of elements connected to the internet. Better control of factories, security systems or even individual sensors is possible through the use of Internet of Things technology. The security of the aforementioned structures and the data they transmit has been a major concern in the development of IoT solutions for wireless data transmission. If we add to this prospect of low-cost end devices, we can seriously consider implementing such solutions in critical infrastructure areas. This article aims to assess the state of the art and experience and identify the main risks and directions for further development in order to improve the cyber security situation of LoRaWAN-based networks. LoRaWAN meets the three key requirements of IoT applications (low cost, large-scale deployability, high energy efficiency) through an open standard and the construction of autonomous networks without third-party infrastructure. However, many research issues remain to be solved/improved such as resource allocation, link coordination, transmission reliability, performance and, above all, security. Thus, we have defined a research gap in the area of LoRaWAN security. The contribution of this work is to structure the knowledge in the field of LoRaWAN security, based on previous publications and our own experience, in order to identify challenges and their potential solutions. This will help move LoRaWAN security research to the next stage.

Digital Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion Technology in Sustainable Urban Governance Networks

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

Time Series Anomaly Detection With Adversarial Reconstruction Networks

Time series data naturally exist in many domains including medical data analysis, infrastructure sensor monitoring, and motion tracking. However, a very small portion of anomalous time series can be observed, comparing to the whole data. Most existing approaches are based on the supervised classification model requiring representative labels for anomaly class(es), which is challenging in real-world problems. So can we learn how to detect anomalous time ticks in an effective yet efficient way, given mostly normal time series data? Therefore, we propose an unsupervised reconstruction model named BeatGAN which learns to detect anomalies based on normal data, or data which majority of samples are normal. BeatGAN provides a framework to adversarially learn to reconstruct, which can cooperate with both 1-d CNN and RNN. Rarely observed anomalies can result in larger reconstruction errors, which are then detected based on extreme value theory. Moreover, data augmentation with dynamic time warping regularizes reconstruction and provides robustness. In the experiments, effectiveness and sensitivity are studied in both synthetic data and various real-world time series. BeatGAN achieves better accuracy and fast inference.

Self-updating Prophecies: An Inquiry into Imagining and Building Decentralized Sensor Networks

Recent years have seen remarkable efforts to decentralize the deployment of infrastructure for sensors and Internet of Things (IoT) applications using emerging low-power protocols. These arrangements are predicated as bottom-up, decentralized, democratic, and participatory while still driven by the corporate logics of lock-in and scaling-up. Reporting on a multisite ethnographic study of a global decentralized IoT initiative, this article explores how a diversity of geographically dispersed contributors reckoned with and negotiated conflicting future visions of globality, locality, and growth. From a socio-material perspective, it argues that the imaginaries and actualizations of decentralization were strategically altered over time to balance unevenness of pace, expectations, and material commitments between different actors. The article offers a new critical perspective to think about infrastructure decentralization as more than the flip side of centralization but as a contingent and mobilizing concept and locates this case in discussions about the future of the Internet.

Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving.

Overview of Big Data Analytics Technologies in Smart Grid

Smart grids have become an essential component of modern society due to their interconnected nature. In the smart grid, unprecedented amounts of data will be created continuously due to the advanced sensor infrastructure. Therefore, analyzing smart grid data is becoming increasingly critical to delivering electricity and managing consumption in the business and physical sectors. Modernizing the grid requires data science, despite the challenges of integrating data analytics into the enterprise. A review of big data management & analysis in the smart grid is presented in this paper. Data analytics and its role in big data management are discussed in this paper along with the challenges of implementing those analytics, and how they can help achieve clean, reliable, and efficient grids. The paper supports Apache Flink due to native streaming for use cases that call for minimal latency, while Apache Spark is better suited for batch data processing.