Ubiquitous Sensor Research Articles

Plasmonic Particle Integration into Near-Infrared Photodetectors and Photoactivated Gas Sensors: Toward Sustainable Next-Generation Ubiquitous Sensing.

Current challenges in environmental science, medicine, food chemistry as well as the emerging use of artificial intelligence for solving problems in these fields require distributed, local sensing. Such ubiquitous sensing requires components with 1) high sensitivity, 2) power efficiency, 3) miniaturizability, and 4) the ability to directly interface with electronic circuitry, i.e., electronic readout of sensing signals. Over the recent years, several nanoparticle-based approaches have found their way into this field and have demonstrated high performance. However, challenges remain, such as the toxicity of many of today's narrow bandgap semiconductors for NIR detection and the high energy consumption as well as low selectivity of state-of-the-art commercialized gas sensors. With their unique light-matter interaction and ink-based fabrication schemes, plasmonic nanostructures provide potential technological solutions to these challenges, leading also to better environmental performance. In this perspective recent approaches of using plasmonic nanoparticles are discussed for the fabrication of NIR photodetectors and light-activated, energy-efficient gas sensing devices. In addition, new strategies implying computational approaches are pointed out for miniaturizable spectrometers, exploiting the wide spectral tunability of plasmonic nanocomposites, and for selective gas sensors, utilizing dynamic light activation. The benefits of colloidal approaches for device fabrication are discussed with regard to technological advantages and environmental aspects, which are barely considered so far.

Leveraging a Variety of Anchors in Cellular Network for Ubiquitous Sensing

Leveraging a Variety of Anchors in Cellular Network for Ubiquitous Sensing

Exploring Digital Biomarkers for Depression Using Mobile Technology

Introduction & BackgroundWith the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms. Objectives & ApproachThe present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants’ Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data. Relevance to Digital FootprintsOur approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints. ResultsWe found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams). Conclusions & ImplicationsLeveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.

From Classification to Clinical Insights

Passively collected behavioral health data from ubiquitous sensors could provide mental health professionals valuable insights into patient's daily lives, but such efforts are impeded by disparate metrics, lack of interoperability, and unclear correlations between the measured signals and an individual's mental health. To address these challenges, we pioneer the exploration of large language models (LLMs) to synthesize clinically relevant insights from multi-sensor data. We develop chain-of-thought prompting methods to generate LLM reasoning on how data pertaining to activity, sleep and social interaction relate to conditions such as depression and anxiety. We then prompt the LLM to perform binary classification, achieving accuracies of 61.1%, exceeding the state of the art. We find models like GPT-4 correctly reference numerical data 75% of the time. While we began our investigation by developing methods to use LLMs to output binary classifications for conditions like depression, we find instead that their greatest potential value to clinicians lies not in diagnostic classification, but rather in rigorous analysis of diverse self-tracking data to generate natural language summaries that synthesize multiple data streams and identify potential concerns. Clinicians envisioned using these insights in a variety of ways, principally for fostering collaborative investigation with patients to strengthen the therapeutic alliance and guide treatment. We describe this collaborative engagement, additional envisioned uses, and associated concerns that must be addressed before adoption in real-world contexts.

Mining fuzzy local periodic activity pattern for Smart home applications

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category.

An Improved Inertial Preintegration Model in Factor Graph Optimization for High Accuracy Positioning of Intelligent Vehicles

Accurate and reliable positioning is of great importance for the realization of intelligent vehicles (IV). Factor graph optimization (FGO) has been popularized in the field of robotics for state estimation. As a ubiquitous sensor, the IMU is widely used for vehicular positioning based on the preintegration theory using FGO. However, the existing preintegration model fails to consider the earth's rotation, which challenges the attitude and positioning performance of vehicles equipped with high-precision inertial measurement units (IMU). To fill this gap, an accurate IMU factor that accounts for the earth's rotation rate is designed in this paper. First, an exact preintegration measurement model is derived based on a high-precision inertial navigation system (INS) mechanization. The proposed factor is more accurate and it enables the application of the FGO more suitable for inertial-based integrated navigation with different precision IMUs. In both simulation and field tests, the INS/ Global Navigation Satellite System (GNSS) integration, as a representative of inertial-based integrated navigation, is used to verify the performance of the proposed preintegration factor. The navigation results using our experimental car reveal that the proposed model leads to a more accurate estimation, outperforming the traditional preintegration models, especially in the aspect of attitude estimation.

Application of Social Sensors in Natural Disasters Emergency Management: A Review

Natural disasters are public emergencies characterized by suddenness, universality, and nonconventionality. Realizing the early warning, monitoring, and intervention of natural disasters and their derivative social impacts is significant for reducing the disasters’ damage and benefits the maintenance of social stability. Social sensors are ubiquitous sensors based on social network platforms. It uses the concepts and methods of physical space to mine social signals that integrate human perception and intelligence in cyberspace. Compared with traditional physical sensors, social sensors represent a crucial data acquisition channel in the emergency management of natural disasters and have the advantages of real time, comprehensive coverage, low cost, and flexible deployment. This article reviews the application of social sensors in natural disasters emergency management. We summarize the application functions of social sensors into three categories: natural disaster situation awareness and event detection, disaster information dissemination and communication, and disaster sentiment analysis and public opinion mining. Based on the above functions, this article analyzes the research status, data, technical methods, and application systems. Finally, this article proposes a research trend of applying social sensors in natural disaster emergency management according to the requirements of real scenarios.

An Air-Ground Coordinated Sensing, Relay and Offloading for Emergency Disposal in ITS System

Nowadays, the Unmanned Aerial Vehicle (UAV) has emerged as a powerful platform for diversified application development. In traffic areas specifically for the much-anticipated Intelligent Transportation System (ITS), UAVs can coordinate with the ground internet of things infrastructure, such as the Road Side Unit (RSU), to perform 3D data collection and processing, improving traffic safety and transportation efficiency. In ITS, decision-making is crucial and relies heavily on proper and timely processing and transmission of the massive amounts of data generated by ubiquitous sensors, especially in emergency disposal scenarios. In this paper, the latency incurred from RSU-aided data offloading and UAV-aided data relay plays a decisive role, characterized by both the average and risk performance metric, Conditional Value at Risk (CVaR), for quantifying the risk incurred from the latency violation over a certain threshold in probability. Specifically, the peak Age of Information (AoI) with its distribution is adopted to underlying the CVaR analysis in relay latency. Besides, the matching algorithm to coordinate the mutual transmission between UAVs and RSUs is proposed, achieving low computation complexity and minimum risk for both sides. The system performance is validated through analysis, simulation, and field experiments.

Overview of Space-Capable Global Navigation Satellite Systems Receivers: Heritage, Status and the Trend towards Miniaturization.

Spaceborne Global Navigation Satellite Systems (GNSS) receivers have become ubiquitous sensors for spacecraft navigation, especially in Low Earth Orbits (LEOs), often also supporting science endeavors or as acting dedicated science payloads. Due to the large number of space-capable GNSS receiver models available, spacecraft designers, as well as scientists, may find it difficult to have or gain an overview of suitable state-of-the-art models for their purposes and constraints. Based on a literature review that included more than 90 different receiver models, this paper aims to provide an overview of space-capable GNSS receivers that have a heritage in space missions. It analyses trends from the collected data and provides an outlook on miniaturized GNSS receiver models, which have a high potential of being used in future space missions.

Estimating pedestrian traffic with Bluetooth sensor technology

ABSTRACT The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research. Bluetooth technology, for example, is already vastly used in vehicular transportation management solutions and services. Current studies discuss the potential of this emerging technology for pedestrian mobility research, but it has yet to be examined in a large urban setting. One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes. This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv, Israel, which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses. We propose a detection methodology that includes system calibration, effective travel time calculation, and classification by velocity that takes into consideration the probability of vehicular traffic jams. An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%. We showcase the results of pedestrian traffic analysis, together with a discussion on the data analysis challenges and limitations. To the best of our knowledge, this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.

Origin and functional diversification of PAS domain, a ubiquitous intracellular sensor.

Signal perception is a key function in regulating biological activities and adapting to changing environments. Per-Arnt-Sim (PAS) domains are ubiquitous sensors found in diverse receptors in bacteria, archaea, and eukaryotes, but their origins, distribution across the tree of life, and extent of their functional diversity are not fully characterized. Here, we show that using sequence conservation and structural information, it is possible to propose specific and potential functions for a large portion of nearly 3 million PAS domains. Our analysis suggests that PAS domains originated in bacteria and were horizontally transferred to archaea and eukaryotes. We reveal that gas sensing via a heme cofactor evolved independently in several lineages, whereas redox and light sensing via flavin adenine dinucleotide and flavin mononucleotide cofactors have the same origin. The close relatedness of human PAS domains to those in bacteria provides an opportunity for drug design by exploring potential natural ligands and cofactors for bacterial homologs.

Ligand-Free Signaling of G-Protein-Coupled Receptors: Physiology, Pharmacology, and Genetics.

G-protein-coupled receptors (GPCRs) are ubiquitous sensors and regulators of cellular functions. Each GPCR exists in complex aggregates with multiple resting and active conformations. Designed to detect weak stimuli, GPCRs can also activate spontaneously, resulting in basal ligand-free signaling. Agonists trigger a cascade of events leading to an activated agonist-receptor G-protein complex with high agonist affinity. However, the ensuing signaling process can further remodel the receptor complex to reduce agonist affinity, causing rapid ligand dissociation. The acutely activated ligand-free receptor can continue signaling, as proposed for rhodopsin and μ opioid receptors, resulting in robust receptor activation at low agonist occupancy with enhanced agonist potency. Continued receptor stimulation can further modify the receptor complex, regulating sustained ligand-free signaling-proposed to play a role in opioid dependence. Basal, acutely agonist-triggered, and sustained elevated ligand-free signaling could each have distinct functions, reflecting multi-state conformations of GPCRs. This review addresses basal and stimulus-activated ligand-free signaling, its regulation, genetic factors, and pharmacological implications, focusing on opioid and serotonin receptors, and the growth hormone secretagogue receptor (GHSR). The hypothesis is proposed that ligand-free signaling of 5-HT2A receptors mediate therapeutic effects of psychedelic drugs. Research avenues are suggested to close the gaps in our knowledge of ligand-free GPCR signaling.

A Low-Power DNN Accelerator Enabled by a Novel Staircase RRAM Array.

Enhancing the ubiquitous sensors and connected devices with computational abilities to realize visions of the Internet of Things (IoT) requires the development of robust, compact, and low-power deep neural network accelerators. Analog in-memory matrix-matrix multiplications enabled by emerging memories can significantly reduce the accelerator energy budget while resulting in compact accelerators. In this article, we design a hardware-aware deep neural network (DNN) accelerator that combines a planar-staircase resistive random access memory (RRAM) array with a variation-tolerant in-memory compute methodology to enhance the peak power efficiency by 5.64× and area efficiency by 4.7× over state-of-the-art DNN accelerators. Pulse application at the bottom electrodes of the staircase array generates a concurrent input shift, which eliminates the input unfolding, and regeneration required for convolution execution within typical crossbar arrays. Our in-memory compute method operates in charge domain and facilitates high-accuracy floating-point computations with low RRAM states, device requirement. This work provides a path toward fast hardware accelerators that use low power and low area.

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

With the ageing of society, the number of households with older individuals or couples living alone is increasing. An “ageing-in-place” approach allows older adults to continue to live at home and receive help only when needed. However, this approach is insufficient for emergencies, such as falls, as well as for individuals with gradually deteriorating health conditions, such as frailty. Unexpected accidents significantly reduce the quality of life (QoL) of older adults. This paper proposes a new framework, the mobility ubiquitous sensor edge environment (MUS3E), to digitally transform ordinary houses to detect the movement of older individuals throughout their home environment and to notify family members and care providers of residents’ health status and safety information. The framework can be easily and inexpensively installed in any home, transforming an ordinary house into a smart home with an automated function for monitoring older residents. It uses ambient sensors such as passive infrared ray sensors to automatically measure health conditions by measuring factors such as walking speed. Residents need not interact with or control the system and can go about their daily lives. Since the sensors used in this system are mass-produced consumer products, they are inexpensive and easily replaceable, as there are many alternatives. In this study, we were able to demonstrate the practically and feasibility of this framework using a prototype that uses open architecture Internet of Things (IoT) software (Debian GNU/Linux 11, Arduino 1.8.19, ESP8266 2.7.4, ESP32 1.0.6, PubSubClient 2.8.0, ESPPerfectTime 0.3.0, mosquitto 2.0.11) components to digitally transform the living environment of older individuals.

Intelligent and trusted metaheuristic optimization model for reliable agricultural network

Artificial intelligence (AI) is gaining demanding growth in the field of smart cities, agriculture, food management, and weather forecasting due to the lack of computing power on sensing devices. The applications of artificial intelligence are integrated with various Internet of Things (IoT) and ubiquitous sensors for the improvement of the agriculture sector and to decrease its management cost. Due to the bounded resources of wireless technologies, most of the solutions are designed for efficient delivery of agriculture data to cloud systems, however, still optimizing the resources management and data load for forwarding nodes, especially those closest to edge boundaries is a challenging issue. Moreover, due to the collection of incorrect environmental data, the decision-making process leads to a decrease in the productivity of the optimization process. To overcome such issues, this work proposes a trustworthy and intelligent agricultural model that uses metaheuristic optimization to enhance resource management to address these problems. The proposed model approach employs the decision-making function to overcome information loss and inconsistency. Moreover, it builds trust in agricultural data collection by using secure IoT devices and facilitating reliable communication. In terms of performance metrics, the proposed model is simulated to assess its importance in comparison to state-of-the-art solutions. It not only collects updated data from agricultural land but also uses artificial intelligence's lightweight optimization technique to reduce the overheads on IoT devices. The experiment findings demonstrate the importance of the proposed model for resource monitoring and overheads on the IoT system.

WMO: an ontology for the semantic enrichment of wetland monitoring data

ABSTRACT Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.

Energy conservation using PISAE and cross-layer-based opportunistic routing protocol (CORP) for wireless sensor network

In wireless sensor networks (WSNs) with constrained sensor node energy, data compression is crucial. Generally speaking, data communication uses up energy. By providing and receiving less data, a sensor node's lifespan is often increased. In this paper, provide an unique unsupervised neural network architecture called Partly-Informed Sparse Autoencoder (PISAE) that tries to reconstruct all sensor readings from chosen prime numbers. This architecture is used to implement the sensor selection approach that we propose. A key challenge for WSN is the selection of the cluster head (CH). Utilizing the K-Medoid, the clustering of sensor nodes is improved. The effect on quality of service (QoS), the location of the sensor nodes, the distance involved, and the energy status requirements are important factors. For the best choice of cluster heads in WSN with regard to distance and energy, the hybridization of two well-known optimization methods, namely Bacteria Foraging Optimization and Harmony Search Algorithm (HSA), is carried out in this study. Opportunistic routing protocol for WSN is proposed in the current work as a cross-layer based opportunistic Routing protocol (CORP). The suggested CORP approach is utilised to identify the best course of action to cut down on computation time and energy usage while enhancing data transmission dependability. Future generations of ubiquitous sensor networks won't have a single AI solution to the problems with energy and load. In addition, this study puts forth the idea of an energy-efficient routing using Fuzzy neural network (ERFN), which can increase the lifespan of WSNs by lowering energy consumption while maintaining energy usage balance. Based on the simulation findings, it can be stated that the proposed CORP approach increases QoS performance metrics such energy consumption, packet delivery rate, packet delay, network lifetime, throughput, and packet loss rate. The performance of the suggested method outperforms that of already-in-use algorithms like FRLDG, MOBFO-EER, and FEEC-IIR.

PrecTime: A deep learning architecture for precise time series segmentation in industrial manufacturing operations

The fourth industrial revolution creates ubiquitous sensor data in production plants. To generate maximum value out of these data, reliable and precise time series-based machine learning methods like temporal neural networks are needed. This paper proposes a novel sequence-to-sequence deep learning architecture for time series segmentation called PrecTime which tries to combine the concepts and advantages of sliding window and dense labeling approaches. The general-purpose architecture is evaluated on a real-world industry dataset containing the End-of-Line testing sensor data of hydraulic pumps. We are able to show that PrecTime outperforms five implemented state-of-the-art baseline networks based on multiple metrics. The achieved segmentation accuracy of around 96% shows that PrecTime can achieve results close to human intelligence in operational state segmentation within a testing cycle.

Enhancing the repeatability and sensitivity of low-cost PCB, pH-sensitive field-effect transistors

Discrete, extended gate pH-sensitive field-effect transistors (dEGFETs) fabricated on printed circuit boards (PCBs) are a low-cost, simple to manufacture analytical technology that can be applied to a wide range of applications. Electrodeposited iridium oxide (IrOx) films have emerged as promising pH-sensitive layers owing to their theoretically high pH sensitivity and facile deposition, but typically exhibit low pH sensitivity or lack reproducibility. Moreover, to date, a combined IrOx and dEGFET PCB system has not yet been realised. In this study, we demonstrate a dEGFET pH sensor based on an extended gate manufactured on PCB that is rendered pH sensitive through an electrodeposited IrOx film, which can reliably and repeatably display beyond-Nernstian pH response. Using a combination of complementary surface analysis techniques, we show that the high pH sensitivity and repeatability of the dEGFETs are dependent on both the chemical composition and critically the uniformity of the IrOx film. The IrOx film uniformity can be enhanced through electrochemical polishing of the extended gate electrode prior to electrodeposition, leading to dEGFETs that exhibit a median pH sensitivity of 70.7 ± 5 mV/pH (n = 56) compared to only 31.3 ± 14 mV/pH (n = 31) for IrOx electrodeposited on non-polished PCB electrodes. Finally, we demonstrate the applicability of these devices by demonstrating the detection and quantification of ampicillin due to β-Lactamase enzyme activity, thus laying the foundation for cheap and ubiquitous sensors which can be applied to a range of global challenges across healthcare and environmental monitoring.

Precision Agriculture Using Soil Sensor Driven Machine Learning for Smart Strawberry Production.

Ubiquitous sensor networks collecting real-time data have been adopted in many industrial settings. This paper describes the second stage of an end-to-end system integrating modern hardware and software tools for precise monitoring and control of soil conditions. In the proposed framework, the data are collected by the sensor network distributed in the soil of a commercial strawberry farm to infer the ultimate physicochemical characteristics of the fruit at the point of harvest around the sensor locations. Empirical and statistical models are jointly investigated in the form of neural networks and Gaussian process regression models to predict the most significant physicochemical qualities of strawberry. Color, for instance, either by itself or when combined with the soluble solids content (sweetness), can be predicted within as little as 9% and 14% of their expected range of values, respectively. This level of accuracy will ultimately enable the implementation of the next phase in controlling the soil conditions where data-driven quality and resource-use trade-offs can be realized for sustainable and high-quality strawberry production.