TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density.

Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset collected using 5 different commercial devices in a fine-grained grid. The dataset contains the matched fingerprints of Received Signal Strength Indicator (RSSI) measurements with the corresponding coordinates, split into training and testing subsets for effortless and fair reproducibility.

Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms.

A Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available as Application-Specific Integrated Circuit (ASIC)s off-the-shelf, each tailored to the requirements of various applications. These chips deliver good performance and low energy consumption but offer customers little-to-no transparency about their internal features. This prevents modification, research in GNSS processing chain enhancement (e.g., application of Approximate Computing (AxC) techniques), and design space exploration to find the optimal receiver for a use case. In this paper, we review the GNSS processing chain using SyDR, our open-source GNSS Software-Defined Radio (SDR) designed for algorithm benchmarking, and highlight the limitations of a software-only environment. In return, we propose an evolution to our system, called Hard SyDR to become closer to the hardware layer and access new Key Performance Indicator (KPI)s, such as power/energy consumption and resource utilization. We use High-Level Synthesis (HLS) and the PYNQ platform to ease our development process and provide an overview of their advantages/limitations in our project. Finally, we evaluate the foreseen developments, including how this work can serve as the foundation for an exploration of AxC techniques in future low-power GNSS receivers.

Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking.

This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics.

Direction of Arrival Method for L-Shaped Array with RF Switch: An Embedded Implementation Perspective.

This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices.

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly.

Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user's perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.

A Survey of Security in Cloud, Edge, and Fog Computing.

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions.

As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated restrictions have exacerbated the loneliness and social-isolation problems. This paper is first and foremost a comprehensive survey of loneliness monitoring and management solutions, from the multidisciplinary perspective of technology, gerontology, socio-psychology, and urban built environment. In addition, our paper also investigates machine learning-based technological solutions with wearable-sensor data, suitable to measure, monitor, manage, and/or diminish the levels of loneliness and social isolation, when one also considers the constraints and characteristics coming from social science, gerontology, and architecture/urban built environments points of view. Compared to the existing state of the art, our work is unique from the cross-disciplinary point of view, because our authors' team combines the expertise from four distinct domains, i.e., gerontology, social psychology, architecture, and wireless technology in addressing the two inter-related problems of loneliness and social isolation in older adults. This work combines a cross-disciplinary survey of the literature in the four aforementioned domains with a proposed wearable-based technological solution, introduced first as a generic framework and, then, exemplified through a simple proof of concept with dummy data. As the main findings, we provide a comprehensive view on challenges and solutions in utilizing various technologies, particularly those carried by users, also known as wearables, to measure, manage, and/or diminish the social isolation and the perceived loneliness among older adults. In addition, we also summarize the identified solutions which can be used for measuring and monitoring various loneliness- and social isolation-related metrics, and we present and validate, through a simple proof-of-concept mechanism, an approach based on machine learning for predicting and estimating loneliness levels. Open research issues in this field are also discussed.

Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter.

This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements' distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA's loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver's tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.

A Comparison of Dyadic and Social Network Assessments of Peer Influence.

The present study compares two methods for assessing peer influence: the longitudinal Actor-Partner-Interdependence-Model (L-APIM) and the longitudinal Social Network Analysis Model (L-SNA). The data were drawn from 1,995 (49% girls; 51 % boys) 3rd grade students (Mage=9.68 years). From this sample, L-APIM (n = 206 indistinguishable dyads; n = 187 distinguishable dyads) and L-SNA (n = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the 3rd and 4th grades. Patterns of statistical significance differed across analyses. Stable distinguishable and indistinguishable L-APIM dyadic analyses identified reciprocated friend influence such that friends with similar levels of mathematical reasoning influenced one another and friends with higher math reasoning influenced friends with lower math reasoning. L-SNA models with an influence parameter (i.e., average reciprocated alter) comparable to that assessed in L-APIM analyses failed to detect influence effects. Influence effects did emerge, however, with the addition of another, different social network influence parameter (i.e., average alter influence effect). The diverging results may be attributed to differences in the sensitivity of the analyses, their ability to account for structural confounds with selection and influence, the samples included in the analyses, and the relative strength of influence in reciprocated best as opposed to other friendships.

Collaborative Indoor Positioning Systems: A Systematic Review.

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system's architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

Evaluation of Adaptive Loop-Bandwidth Tracking Techniques in GNSS Receivers.

GNSS receivers use tracking loops to lock onto GNSS signals. Fixed loop settings limit the tracking performance against noise, receiver dynamics, and the current scenario. Adaptive tracking loops adjust these settings to achieve optimal performance for a given scenario. This paper evaluates the performance and complexity of state-of-the-art adaptive scalar tracking techniques used in modern digital GNSS receivers. Ideally, a tracking channel should be adjusted to both noisy and dynamic environments for optimal performance, defined by tracking precision and loop robustness. The difference between the average tracking jitter of the discriminator's output and the square-root CRB indicates the loops' tracking capability. The ability to maintain lock characterizes the robustness in highly dynamic scenarios. From a system perspective, the average lock indicator is chosen as a metric to measure the performance in terms of precision, whereas the average number of visible satellites being tracked indicates the system's robustness against dynamics. The average of these metrics' product at different noise levels leads to a reliable system performance metric. Adaptive tracking techniques, such as the FAB, the FL, and the LBCA, facilitate a trade-off for optimal performance. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. All three methods steer a third-order adaptive PLL and are tested in simulated scenarios emulating static and high-dynamic vehicular conditions. The measured tracking performance, system performance, and time complexity of each algorithm present a detailed analysis of the adaptive techniques. The results show that the LBCA with a piece-wise linear approximation is above the other adaptive loop-bandwidth tracking techniques while preserving the best performance and lowest time complexity. This technique achieves superior static and dynamic system performance being 1.5 times more complex than the traditional tracking loop.

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios-A Systematic Review.

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.

Effects of Multipath Attenuation in the Optical Communication-Based Internet of Underwater Things.

The propagation of light underwater is tied closely to the optical properties of water. In particular, the underwater channel imposes attenuation on the optical signal in the form of scattering, absorption, and turbulence. These attenuation factors can lead to severe spatial and temporal dispersion, which restricts communication to a limited range and bandwidth. In this paper, we propose a statistical model to estimate the probability density function of the temporal dispersion in underwater wireless optical communication (UWOC) based Internet of Underwater Things (IoUTs) using discrete histograms. The underwater optical channel is modeled using Monte Carlo simulations, and the effects of temporal dispersion are presented by measuring the magnitude response of the channel in terms of received power. The temporal response analysis is followed by an extensive performance evaluation in terms of bit error rate (BER). To facilitate in-depth theoretical analysis, we have measured and presented magnitude response and BER of the channel under different field-of-views (FoVs), apertures, and water types. The three main areas under study are (i) BER versus link distance behavior, (ii) temporal response of the channel, and (iii) effect of scattering on photon travel. Our study shows the two main factors that contribute to beam spreading and temporal dispersion are (i) diffusivity of the optical source and (ii) multiple scattering. Furthermore, our results suggest that temporal dispersion caused due to multiple scattering cannot be mitigated completely; however, it can be minimized by optimizing the receiver aperture.

Early Cognitive Precursors of Children's Mathematics Learning Disability and Persistent Low Achievement: A 5-Year Longitudinal Study.

Mathematical difficulties have been distinguished as mathematics learning disability (MLD) and persistent low achievement (LA). Based on 1,880 Finnish children who were followed from kindergarten (age 6) to fourth grade, this study examined the early risk factors for MLD and LA. Distinct groups of MLD (6.0% of the sample) and LA (25.7%) children were identified on the basis of their mathematics performance between first and fourth grades with latent class growth modeling. Impairment in the same set of cognitive skills, including language, spatial, and counting skills, was found to underlie MLD and LA. The finding highlights the importance of monitoring mathematical development across the early grades and identifying early cognitive precursors of MLD and LA for screening and intervention efforts.

Teacher-student interaction and lower secondary school students' situational engagement.

BACKGROUND: Prior research has shown that engagement plays a significant role in students' academic learning. AIMS: The present study sought to expand the current understanding of students' engagement by examining how situational engagement during a particular lesson is associated with the observed teacher-student classroom interactions (i.e., emotional support, instructional support, and classroom organization) in the same lesson. SAMPLE: The participants were 709 Grade 7 students (47.7% girls) from 59 classrooms in 26 lower secondary schools and 51 teachers. METHODS: The data consisted of 155 video-recorded lessons (90 language arts and 65 mathematics lessons) coded using the Classroom Assessment Scoring System - Secondary (CLASS-S) observational instrument. Students' self-ratings of their situation-specific engagement were collected using the mobile-based In Situations (InSitu) Instrument at the end of each lesson. The data were analyzed with cross-classified two-level hierarchical modelling. RESULTS AND CONCLUSIONS: The results indicated that emotional support in the classroom was positively associated with students' emotional engagement and help-seeking, whereas classroom organization was associated with students' behavioural and cognitive engagement. Overall, the findings provide novel evidence suggesting that students' engagement can be fostered by supportive teacher-student interactions.

Parent contributions to friendship stability during the primary school years.

The present study examines whether characteristics of parents predict the stability of a child's best friendships across the primary school years. Participants included 1,523 Finnish children (766 boys) who reported involvement in a total of 1,326 reciprocated friendship dyads in the 1st grade (M = 7.16 years old). At the onset of the study, mothers and fathers completed questionnaires describing their own parenting (i.e., behavioral control, psychological control, and affection toward the child) and depressive symptoms. Child scores for peer status (i.e., acceptance and rejection) were derived from 1st grade peer nomination data. Discrete-time survival analyses predicted the occurrence and timing of friendship dissolution, across 1st to 6th grades, for friendships that began in 1st grade. Parent depression and parent psychological control uniquely predicted subsequent child friendship dissolution, above and beyond the contribution of peer status variables. (PsycINFO Database Record

Knowing, applying, and reasoning about arithmetic: Roles of domain-general and numerical skills in multiple domains of arithmetic learning.

The longitudinal relations of domain-general and numerical skills at ages 6-7 years to 3 cognitive domains of arithmetic learning, namely knowing (written computation), applying (arithmetic word problems), and reasoning (arithmetic reasoning) at age 11, were examined for a representative sample of 378 Finnish children. The results showed that domain-general skills, including spatial visualization, language, rapid automatized naming, and working memory, contributed independently to arithmetic learning. These relations were mostly mediated via basic number competence (i.e., counting sequence and number system knowledge), although spatial visualization remained predictive of arithmetic outcomes. The findings underscore a similar developmental course of arithmetic learning across different cognitive domains where domain-general skills build a launchpad for advanced arithmetic via enhancing basic number competence. (PsycINFO Database Record

Cross-lagged relations between teacher and parent ratings of children's task avoidance and different literacy skills.

BACKGROUND: Task avoidance is a significant predictor of literacy skills. However, it remains unclear whether the relation between the two is reciprocal and whether it is affected by the type of literacy outcome, who is rating children's task avoidance, and the children's gender. AIM: The purpose of this longitudinal study was to examine the cross-lagged relations between teacher and parent ratings of children's task avoidance and different literacy skills. SAMPLE: One hundred and seventy-two Greek children (91 girls, 81 boys) were followed from Grade 1 to Grade 3. METHODS: Children were assessed on reading accuracy, reading fluency, and spelling to dictation. Parents and teachers rated the children's task-avoidant behaviour. RESULTS: Results of structural equation modelling showed that the cross-lagged relations varied as a function of the literacy outcome, who rated the children's task avoidance, and children's gender. Earlier reading and spelling performance predicted subsequent parent-rated task avoidance, but parent-rated task avoidance did not predict subsequent reading and spelling performance (with the exception of spelling in Grade 3). Teacher-rated task avoidance and reading fluency/spelling had a reciprocal relationship over time. In addition, the effects of teacher-rated task avoidance on future spelling were significantly stronger in boys than in girls. CONCLUSIONS: This suggests that poor reading and spelling performance can lead to subsequent task avoidance in both classroom and home situations. The fact that task avoidance permeates across different learning environments is alarming and calls for joint action from both parents and teachers to mitigate its negative impact on learning.

Parental causal attributions and emotions in daily learning situations with the child.

This study investigated the dynamics between the causal attributions parents reported daily for their children's success in learning situations and parental positive emotions. The sample consisted of 159 mothers and 147 fathers of 162 first graders (83 girls, 79 boys; aged from 6 to 7 years, M = 7.5 years, SD = 3.6 months). Parents filled in a structured diary questionnaire concerning their causal attributions and emotions over 7 successive days in the fall semester and again over 7 successive days in the spring semester. Multilevel analyses showed that both parental causal attributions and positive emotions varied more within parents (between days over the week) than between parents. Furthermore, mothers' positive emotions on a certain day predicted their causal attributions on that same day rather than vice versa. The higher the level of positive emotions parents reported in a specific day, the more they used effort and ability as causal attributions for their offspring's success on that same day.

Stable same-sex friendships with higher achieving partners promote mathematical reasoning in lower achieving primary school children.

This study was designed to investigate friend influence over mathematical reasoning in a sample of 374 children in 187 same-sex friend dyads (184 girls in 92 friendships; 190 boys in 95 friendships). Participants completed surveys that measured mathematical reasoning in the 3rd grade (approximately 9 years old) and 1 year later in the 4th grade (approximately 10 years old). Analyses designed for dyadic data (i.e., longitudinal actor-partner interdependence model) indicated that higher achieving friends influenced the mathematical reasoning of lower achieving friends, but not the reverse. Specifically, greater initial levels of mathematical reasoning among higher achieving partners in the 3rd grade predicted greater increases in mathematical reasoning from 3rd grade to 4th grade among lower achieving partners. These effects held after controlling for peer acceptance and rejection, task avoidance, interest in mathematics, maternal support for homework, parental education, length of the friendship, and friendship group norms on mathematical reasoning.