Ubiquitous Monitoring Research Articles

Artificial General Intelligence for the Detection of Neurodegenerative Disorders

Parkinson’s disease and Alzheimer’s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

Constructed rock riffles increase habitat heterogeneity but not biodiversity in streams constrained by urban impacts

AbstractRiffles are heterogeneous habitats that support diverse assemblages in natural streams. They are often constructed as part of stream restoration practice, including in degraded urban stream ecosystems, despite larger scale limits to ecological state. Such restoration practices fail to consider ecological theory, and their outcomes have rarely been robustly monitored and assessed. We assessed the effects of constructed rock riffles on habitat heterogeneity and on macroinvertebrate assemblages in six urban streams (4%–32% effective imperviousness). We compared habitat heterogeneity, and taxon abundance and richness of the six streams before and after (1 and 5 years) riffle construction and in three control streams without riffles. Riffles increased habitat heterogeneity. In contrast, macroinvertebrate assemblages in all streams were dominated by tolerant, cosmopolitan, and invasive taxa before and after riffle construction. Riffles reduced the abundance and richness of tolerant taxa in less urbanized streams and increased their richness in more urban streams. These changes had no effect on measures of biodiversity. Urban stormwater runoff can affect the degree to which rock riffles increase habitat heterogeneity through sediment transport and increased physical disturbance. In highly urban streams, increased habitat heterogeneity is likely to primarily favor cosmopolitan species that can tolerate frequent chemical and physical disturbance. Riffle construction may reduce the abundance of cosmopolitan species and increase the abundance of more sensitive species, but only if catchment‐scale impacts of urban stormwater runoff are adequately controlled. Robust experiments such as this add to general understanding of the effectiveness of restoration practice, reducing the need for ubiquitous monitoring of restoration projects.

A Multi-Center Clinical Trial for Wireless Stethoscope-Based Diagnosis and Prognosis of Children Community-Acquired Pneumonia.

Community-Acquired Pneumonia (CAP) is a significant cause of child mortality globally, due to the lack of ubiquitous monitoring methods. Clinically, the wireless stethoscope can be a promising solution since lung sounds with crackles and tachypnea are considered as the typical symptoms of CAP. In this paper, we carried out a multi-center clinical trial in four hospitals to investigate the feasibility of using a wireless stethoscope for children CAP diagnosis and prognosis. The trial collects both the left and right lung sounds from children with CAP at the time of diagnosis, improvement, and recovery. A bilateral pulmonary audio-auxiliary model (BPAM) is proposed for lung sound analysis. It learns the underlying pathological paradigm for the CAP classification by mining the contextual information of audio while preserving the structured information of breathing cycle. The clinical validation shows that the specificity and sensitivity of BPAM are over 92% in both the CAP diagnosis and prognosis for the subject-dependent experiment, over 50% in CAP diagnosis and 39% in CAP prognosis for the subject-independent experiment. Almost all benchmarked methods have improved performance by fusing left and right lung sounds, indicating the direction of hardware design and algorithmic improvement.

Deep Reinforcement Learning for Aerial Data Collection in Hybrid-Powered NOMA-IoT Networks

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The hybrid-powered networks composed of wireless-powered communication (WPC) terminal and solar-powered UAV can solve the energy supply problem of the IoT networks, and the nonorthogonal multiple access (NOMA) technique can solve the massive access problem of IoT terminals. Exploiting these benefits, we investigate joint UAV 3-D trajectory design and time allocation for aerial data collection in hybrid-powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, Quality of Service (QoS) requirements, and flight conditions. The problem is nonconvex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called fair communication is accomplished by trajectory design and time allocation (FC-TDTA), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that our proposed algorithm performs better than benchmarks in fair throughput maximization. The proposed FC-TDTA algorithm can make the UAV: 1) fly in appropriate direction and speed, so that the UAV can arrive at the charging station before the energy runs out and 2) conduct WPC energy transmission and data collection to achieve fair communication.

Slow-Light Enhanced Liquid and Gas Sensing Using 2-D Photonic Crystal Line Waveguides—A Review

Photonic crystals (PhCs) are periodically structured dielectric materials that have attracted significant research interest in the last two decades for their ability to slow down the group velocity of a propagating pulse envelope with promising sensing applications. This review article discusses the properties of 2-D PhCs including the slow-light phenomenon, bandgap generation, and application of these properties for gas and liquid sensing. Waveguide generation by introducing defects with light guiding and confinement is discussed. In addition, for 2-D PhC line waveguides, a comprehensive review on the slow-light principle and phenomenon of slow-light enhanced sensing for gases and liquids is discussed. 1-D and 3-D PhCs are also reviewed for bandgap generation and defects in PhCs along with present fabrication challenges and future trends. Our study highlights an increase in the detection capabilities of PhC-based sensors paving way for high-sensitivity detectors with applications in ubiquitous monitoring of gases and liquids.

O³HSC: Outsourced Online/Offline Hybrid Signcryption for Wireless Body Area Networks

Wireless body area networks (WBAN) enable ubiquitous monitoring of patients, which can change the future of healthcare services overwhelmingly. As the collected data of patients usually contain sensitive information, how to collect, transfer, store and share data securely and properly has become a concerning issue. Attribute-based encryption (ABE) can achieve data confidentiality and fine-grained access control simultaneously. Identity-based ring signature (IBRS) allows patients to prove their identity without leaking any extra (private) information. However, the heavy computational burden of ABE and IBRS is intolerable for most power-limited mobile devices, which account for a large proportion of WBAN devices. This paper combines the attribute-based online/offline encryption (ABOOE) and IBRS to achieve an outsourced online/offline hybrid signcryption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC) scheme. As far as we know, this scheme is the first signcryption scheme that adopts IBRS and satisfies online/offline signcryption simultaneously. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC divides the key generation and signcryption into offline and online phases to increase the throughput of the central authority and save the power resources of mobile devices, respectively. Besides, outsourced decryption and public signature verification are also realized. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> achieves security under CCA and CMIA, and the performance analysis shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> is a lightweight and applicable scheme for WBAN.

Unobtrusive Measurement of Physiological Features Under Simulated and Real Driving Conditions

Objective: For driver state estimation, physiological features might be promising input parameters. As cable-bound sensing of these parameters is impractical for ubiquitous monitoring, the measurement certainly has to be based on unobtrusive and contact-free technologies. In this work, unobtrusive methods for heart rate (HR) and respiration rate (RR) monitoring, including a hybrid imaging approach, are evaluated under simulated and real driving conditions. Methods: The feasability of unobtrusive methods was tested by comparing measurements from unobtrusive sensors to reference sensors. Under laboratory conditions, magnetic induction and photoplethysmography, both integrated into the seat belt, and hybrid imaging, combining visual and thermal imaging, were evaluated for RR sensing. In real driving, creating an urban and a rural scenario, sensing of RR by hybrid imaging and sensing of HR by a seat-integrated capacitive ECG were evaluated. Results: Under laboratory conditions, a reliable RR detection was possibly using all three sensor technologies. In real-world driving, a reliable HR and RR detection was possible during the rural scenario. In the urban scenario, only the RR detection was feasible. Due to motion artifacts, the capacitive ECG was disturbed and the HR detection impaired. Conclusion: The evaluated unobtrusive measurement systems can monitor physiological parameters during e.g. long-time driving on highways, but may not yet be feasible for monitoring during agile inner-city driving situations, due to motion artifacts. Therefore, future work should focus on artifact reduction. Significance: Physiological features might be used as input parameters for driver state estimation systems. This work presents unobtrusive sensing methods for these parameters.

Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio (<inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula>) and percentage reduction in motion artifacts (<inline-formula> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula>). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average <inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> values of 23.81 dB and 57.01&#x0025;, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average <inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> values of 15.97 dB and 39.01&#x0025;, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Energy-Saving Techniques for Urban Noise WSN With Kalman-Based State Estimation and Green Facade Energy Harvester

In this article, a Kalman framework is proposed for dynamic energy-saving in wireless sensor networks used to monitor urban noise pollution. The energy-saving framework implements a dynamic power management strategy (DPMS) with the Kalman algorithm that varies the sensor node&#x2019;s sleep period according to the measured noise levels. An Internet-of-Things (IoT) edge-based self-sustaining long-range (LoRa) network is developed and used for ubiquitous monitoring and analysis of urban noise pollution. The network consists of a star topology with six LoRa battery-free wireless sensing nodes deployed in M&#x00E9;rida city downtown, each node powered by a green facade structure consisting of an array of plant-microbial fuel cells (P-MFC). The sensor node&#x2019;s prototype was implemented following Mexican regulations, transmitting the data packets with the open frequency band of 915 MHz, and monitoring the LoRa network from a web page. Experimental results prove a sustainable operation with a green facade P&#x2013;MFC array power generation of 112.1 mW with an open-circuit voltage of 2.7 V and a short-circuit current of 180 mA. The sensor node&#x2019;s average power consumption is 11.2 mW; therefore, sufficient energy is generated for continuous monitoring. The efficient Kalman DPMS is also tested with the urban noise measurement estimation and adjusting the sleep period only if the urban measurement state estimation is above the threshold normativity. The system&#x2019;s low-power consumption allows to perform 70 continuous LoRa transmissions even when the energy harvester source is absent, and the power capacity of the green facade restores a supercapacitor full charge after only 4 min of a LoRa transmission. A 23.6&#x0025; of energy was saved with Kalman DPMS in comparison with a continuous measurement system of 10-min uniform-sleep period.

Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review.

Background:The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak.Methods:This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model.Results:In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at “on-body,” “in-clinic/hospital,” and even “in-community” levels.Conclusions:The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.

Lab-on-printed circuit board based water harvesting condensation particle counter for ubiquitous monitoring of airborne ultrafine particles

We have developed a water harvesting condensation particle counter (WHCPC) using lab-on-printed circuit board (PCB) technology. Our method provides an inexpensive, compact, and highly accurate system for the ubiquitous monitoring of airborne ultrafine particles (UFPs). Lab-on-PCB-based WHCPC uses water as the working fluid to monitor harmless UFPs; it does not require water replenishment using a moderated water condensation method. Lab-on-PCB-based WHCPC consists of two main parts, a UFP growth section and a mini-OPC, through which UFPs are grown into micro-sized droplets and the grown droplets are single-counted. The UFP growth section consists of two PCBs and a 3D-printed channel. The heater, temperature sensor, and hydrophilic micropillar array wick, which are essential for the growth of UFPs, are integrated on the PCBs. Simulations were performed to verify the saturated-air generation and water collection in the UFP growth section. The dew points of the inlet and outlet air samples were measured to verify water collection using the moderated water condensation method. Through quantitative experiments using UFPs artificially generated in the laboratory, particles of 9.4 nm could be counted. It was possible to precisely measure the number concentration (2500–270000 N cm−3) over a wide range. In addition, the stability of the proposed system was confirmed through a long-term comparison with the reference CPC in an outdoor environment. The proposed system realized the sensorization of CPC because it could monitor harmless UFPs, with minimal maintenance; the proposed system is also inexpensive and compact.

Deep learning in pervasive health monitoring, design goals, applications, and architectures: An overview and a brief synthesis

The continuous growth of an aging population in some countries, and patients with chronic conditions needs the development of efficient solutions for healthcare. Pervasive Health Monitoring (PHM) is an important pervasive computing application that has the potential to provide patients with a high-quality medical service and enable quick-response alerting of critical conditions. To that end, PHM enables continuous and ubiquitous monitoring of patients' health and wellbeing using Internet of Things (IoT) technologies, such as wearables and ambient sensors. In recent years, deep learning (DL) has attracted a growing interest from the research community to improve PHM applications. In this paper, we discuss the state-of-the-art of DL-based PHM, through identifying, (1) the main PHM applications where DL is successful, (2) design goals and objectives of using DL in PHM, and (3) design notes including DL architectures and data preprocessing. Finally, main advantages, limitations and challenges of the adoption of DL in PHM are discussed.

Smartphone-Based Tapping Frequency as a Surrogate for Perceived Fatigue

Fatigue is a common symptom in various diseases, including multiple sclerosis (MS). The current standard method to assess fatigue is through questionnaires, which has several shortcomings; questionnaires are subjective, prone to recall bias, and potentially confounded by other symptoms like stress and depression. Thus, there is an unmet medical need to develop objective and reliable methods to evaluate fatigue. Our study seeks to develop an objective and ubiquitous monitoring tool for assessing fatigue. Leveraging a smartphone-based rapid tapping task, we conducted a two-week in-the-wild study with 35 MS patients. We explore the association between tapping derived metrics and perceived fatigue assessed with two standard clinical scales: fatigue severity scale (FSS) and fatigue scale for motor and cognitive function (FSMC). Our novel smartphone-based fatigue metric, mean tapping frequency, objectively ranks perceived fatigue with a mean AUCROC = .76, CI = [.71, .81] according to the FSMC, and a mean AUCROC = .81, CI = [.76, .86] according to the FSS. These results demonstrate that our approach is feasible and valid in uncontrolled environments. In this work, we provide a promising tool for objective fatigue monitoring to be used in clinical trials and routine medical care.

(Invited) New Design Rules of Gas Sensors Enhance Their Reliability for End-User Acceptance

Contemporary gas monitoring scenarios for industrial safety, environmental surveillance, medical diagnostics, personal wellness, and other applications demand sensors with higher accuracy, enhanced stability, and often lower power; all in unobtrusive formats and at low cost [1]. Unfortunately, available sensors based on traditional detection principles often have not only inadequate accuracy and stability but also have relatively high power demands, pushing the limits of existing detection concepts where we are reaching their fundamental performance limits. These limitations of available sensors drive the innovative designs of new generation of sensors.This talk will stimulate your senses by (1) posing fundamental questions on principles of gas sensing and (2) by demonstrating on how modern multidisciplinary research addresses these questions by building sensors with previously unthinkable capabilities.We will present new sensor-design criteria that allow high sensor stability and multi-gas detection with individual sensors. We brought new gas-sensing capabilities to very popular semiconducting metal oxide (SMOX) materials by introducing our dielectric excitation scheme of these materials, which is a contemporary alternative to classic resistance measurements from 1960-s [2]. Our excitation scheme based on contemporary electronics brought highly desired features such as linear sensor response (R^2 > 0.99), dynamic range of six decades of gas concentrations, 50-fold improvement in the limit of detection, and cancelled effects from ambient temperature over - 25 to 50 oC. We lab-tested our excitation scheme with numerous volatiles and did field validations in wireless sensor network, drone-based, and wearable formats. We launched a product with one of our partners and are working toward diverse consumer, industrial, medical, homeland security, and other applications.We will conclude with a perspective for future needs and with the 2050 roadmap for ubiquitous gas monitoring.[1] Potyrailo, R. A. Multivariable sensors for ubiquitous monitoring of gases in the era of Internet of Things and Industrial Internet, Chem. Rev. 2016, 116, 11877–11923.[2] Potyrailo, R. A.; Go, S.; Sexton, D.; Li, X.; Alkadi, N.; Kolmakov, A.; Amm, B.; St-Pierre, R.; Scherer, B.; Nayeri, M.; Wu, G.; Collazo-Davila, C.; Forman, D.; Calvert, C.; Mack, C.; Mcconnell, P. Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation, Nat. Electron. 2020, 3, 280–289.

Factors that influence user perception of ubiquitous monitoring environments: An empirical study in a developing country

Over the years, conventional monitoring devices such as video cameras and tape-recorders have been redesigned into smarter and smaller forms which can be integrated seamlessly into an environment. The purpose of these ubiquitous monitoring devices is to enable the provision of innovative applications and services that support user wellbeing. Despite improving operations in essential areas such as health, there are still concerns associated with ubiquitous monitoring. For benefits associated with ubiquitous monitoring to be fully realized, there is the need to understand the role of user perceptions. This study investigates the factors that influence user perceptions of ubiquitous monitoring devices by drawing samples from a developing country. Users’ response on seven recurring ubiquitous monitoring perceptions were collected using a survey questionnaire. The relationships among these factors were analysed using Partial Least Square Structural Equation Modelling. The results suggest a significant relationship between Perceived Natural Border Crossing and Perceived Privacy Invasion. Also Perceived Affordance, Perceived Coverage and Perceived Privacy Invasion predicted Perceived Trust. The findings imply that more emphasis must be given to educating and familiarizing users with ubiquitous monitoring devices. Future studies are expected to replicate the study in other developing societies to validate these claims.

Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants

Widespread use of machine learning (ML) systems could result in an oppressive future of ubiquitous monitoring and behavior control that, for dialogic purposes, we call “Informania.” This dystopian future results from ML systems’ inherent design based on

Urban Analytics: History, Trajectory, and Critique

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

A hybrid energy-efficient routing protocol for wireless body area networks using ultra-low-power transceivers for eHealth care systems

One of the major applications of Wireless Body Area Networks (WBANs), and the Internet of Things is the Electronic Health-Care systems. The progress in WBANs, and implanted health monitoring technologies have strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. An energy-efficient protocol is an important factor in the operation of eHealth care systems. A hybrid energy-efficient routing protocol is proposed in this paper for the distributed wireless body area network. In which a hybrid communication method and a new synchronization scheme are discussed. The hybrid communication enhances the network lifetime, and the synchronization scheme will consume less energy by avoiding collisions. The performance of the proposed protocol is analyzed, and it is compared with three baseline routing protocols at the 2.4 GHz ISM band with two ultra-low power transceivers.

SpeedTalker: Automobile Speed Estimation via Mobile Phones

Among all the road accidents, speeding is the most deadly factor. To reduce speeding, it is essential to devise efficient schemes for ubiquitous speed monitoring. Traditional approaches either suffers from using special equipment or special deployment. In this paper, we propose SpeedTalker, a mobile phone-based approach to perform speed detection on automobiles. SpeedTalker estimates the automobile speed by passively sensing the acoustic and image signals. Specifically, we use the time difference of arrivals (TDOA) model based on acoustic signals to figure out the candidate trajectories of automobile, and use the pin-hole model based on image frames to figure out the vertical distance between the user's position and the automobile's trajectory, thus to estimate the unique trajectory. Besides, we propose a method to effectively mitigate the influence of the movement jitters of mobile phone. We implemented a system prototype for SpeedTalker and estimated the automobile speed with high accuracy. Experiment results show that in the scenario of single automobile, SpeedTalker can achieve an average estimation error of 6.1% compared to radar speed guns. In the scenario of multiple automobiles, SpeedTalker can achieve an average estimation error of 9.8%, which is acceptable for usage.