RDTAS-MAC: A Cross-Layer Robust Data Collection Protocol for AUV-Assisted UASNs Under Node Position Uncertainty

Vehicular sensor networks (VSNs) have emerged as a new technology, which combines wireless communication offered by vehicular ad hoc NETworks with sensing devices installed in vehicles or in roadsides. VSN creates a huge opportunity to extend the roadside sensor infrastructure of existing traffic control systems. The main issues in VSNs are inherent in the data collection and its dissemination in such a large-scale network, characterized by frequent topology changes and network partitioning. This paper proposes a new multi-hop data collection and dissemination protocol based on data replication approach. The aim is an efficient collection and dissemination of data from a defined geographic area. Differently from other data collection protocols for VSNs, which have for purpose to collect data for traffic control applications or monitoring applications, our proposed protocol called Collaborative Data Collection Protocol (CDCP for VSNs) focuses on collecting data to be used in non-delay tolerant applications, so CDCP aims to collect the requested data within an optimized delay, but also, focuses in making this data accessible for other potential requests. Simulation results show the effectiveness of our proposed protocol.

One of the fundamental differences between the terrestrial radio frequency (RF) signal and underwater acoustic signal is long propagation delay. This paper is focused on a scheduling scheme for regional data collection in underwater acoustic sensor networks. Long propagation delay of the acoustic signal causes a serious problem for transmission scheduling. In this paper, we propose an efficient transmission scheduling algorithm using the property of long propagation delay for real-time data collection which is critical to tactical surveillance applications. We show through the simulation that the proposed scheme reduces total transmission time and increases the network efficiency significantly.

Sensor network has found critical applications in extreme environments. However, in the extreme environments, a predictable and stable path may never exist, since the transient network connectivity, asymmetric links and unstable nodes are prevalent. Thus, the extreme environments severely challenge its basic function of data collection. Particularly, those disruptive conditions make traditional data collection protocols inefficient. In this paper, we design Opportunistic Network Erasure Coding (ONEC) protocol for collaborative data collection in disruptive sensor networks. The idea behind ONEC protocol is to study how each node determines code degree distribution and receding strategies in a distributed fashion, so that all data of network can be recovered with high probability upon receiving any sufficient amount of encoded packets. First, each node derives code degree distribution through recursive discrete deconvolution. Second, every node conducts selective receding of its own sensing data and opportunistically received data. Last, the ONEC ensures decoder can recover all data from any sufficient amount of encoded packets with high probability. The performance evaluations through extensive simulation validate that ONEC can truly achieve efficient data collection with high reliability in disruptive sensor networks and outperform other existing approaches in terms of network goodput, message complexity and buffer space.

A protocol for the collection of vector data in Volunteered Geographic Information (VGI) projects is proposed. VGI is a source of crowdsourced geographic data and information which is comparable, and in some cases better, than equivalent data from National Mapping Agencies (NMAs) and Commercial Surveying Companies (CSC). However, there are many differences in how NMAs and CSC collect, analyse, manage and distribute geographic information to that of VGI projects. NMAs and CSC make use of robust and standardised data collection protocols whilst VGI projects often provide guidelines rather than rigorous data collection specifications. The proposed protocol addresses formalising the collection and creation of vector data in VGI projects in three principal ways: by manual vectorisation; field survey; and reuse of existing data sources. This protocol is intended to be generic rather than being linked to any specific VGI project. We believe that this is the first protocol for VGI vector data collection that has been formally described in the literature. Consequently, this paper shall serve as a starting point for on-going development and refinement of the protocol.

Smartphone-based data collection from wireless sensor networks in an urban environment

BackgroundWe present a digital phenotyping protocol designed to continuously and objectively measure behavioral, physiological, and contextual data during pregnancy and the postpartum period using passive sensing from Garmin smartwatches and smartphones, along with active ecological momentary assessments (EMAs). This novel protocol uniquely adapts to the unpredictable timing of childbirth, spanning from the third trimester through 6 weeks post partum, to accurately capture critical temporal changes and maternal-infant outcomes. By providing high-frequency real-time data, this methodology offers comprehensive insights into pregnancy-related behaviors and physiological processes, overcoming the limitations of traditional retrospective self-report methods.ObjectiveWe aim to develop a protocol for longitudinal data collection supporting digital phenotyping that is optimized for pregnancy and the postpartum period. This protocol leverages the pregnant population’s heightened interest in health and tracking. It aims to minimize the burden on the participants, increase retention, and assess the value of wearables compared to smartphones to determine the appropriate data collection methods.MethodsData will be collected from 30 nulliparous participants from the start of the third trimester through 6 weeks post partum. This protocol uses 3 distinct 1-time surveys, alongside daily and weekly EMAs, to capture real-time maternal experience data. Passive maternal data—such as activity, vitals, sleep, and location—are collected via smartphones and Garmin smartwatches. Participants are expected to log data about the newborn after delivery through the mobile app Huckleberry. This protocol was developed in collaboration with the Northeastern University Sath Laboratory, which focuses on digital phenotyping and longitudinal data collection, and the Tufts Medical Center’s obstetrics and gynecology department, which has expertise in working with the pregnant population.ResultsThis study was funded in August 2024. Data collection is projected to run from October 2025 to July 2026. As of September 2025, the study has been approved, and recruitment and data collection are to begin. The results are expected to be published by August 2026. We plan to assess the retention rates, survey and EMA completion rates, wear time of the smartwatch without intervention, and data volume logged in the Huckleberry app. In addition, we will perform digital phenotyping to determine whether the data collected during pregnancy can be used to predict breastfeeding outcomes, delivery outcomes, and maternal-infant well-being.ConclusionsThis protocol integrates the use of digital phenotyping in pregnancy and postpartum research, providing a novel method for capturing real-time indicators of maternal well-being. It will determine the expected rates of data completion and appropriate sample size using a power analysis for a more extensive future study. By integrating smartphone and wearable sensor data, this protocol has the potential to transform the way maternal health clinical interventions are designed and implemented in the future.International Registered Report Identifier (IRRID)PRR1-10.2196/77175

Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1–20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward’s clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.

Currently, there are no public, universal or easily applied protocols for capturing body scan data, which may result in discrepancies in the captured scan data; a series issue in a scientific field. This paper presents a step-by-step process that forms the basic procedure for capturing, storing, processing, and using body scan data based on that currently used at The University of Manchester. Here, the entire scan capturing protocol is explained from recruitment through to the scan process itself and final following up on the participant postscanning. This process has allowed the Apparel Design Engineering cluster within the School of Materials to increase the repeatability, accuracy, and professionalism of their work in 3D Body Scan research, with the intention to disseminate this protocol throughout research and industry.

Data collection is one of the basic tasks of WSN, and data collection protocol occupies a pivotal position in WSN. Data collection protocol in TinyOS 2.0 is implemented in the form of Collection Tree Protocol (CTP). However, CTP have proved to be extremely efficient [1], in practical applications, CTP also have some disadvantages. The paper presents an improved data collection protocol ICTP based on the analysis of CTP. The protocol introduces the concept of load balance into CTP. Finally, with the simulation results, we have proved that our improvement improves the efficiency and performance of the data collection task in WSN. There is something noteworthy that ICPT doesn't have additional energy as a result of not selecting the optimal path.

Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).

Background: In low-income settings, cardiovascular data collection faces low adherence and poor-quality data owing to cultural mismatches and a possible lack of trust in conventional modes of data collection. We propose a culturally adapted and trust-enhanced biometric protocol to improve cardiovascular data collection in low-income populations. Methodology: We developed a culturally adapted and trust-enhanced biometric protocol integrating three interconnected modules: (1) cultural belief mapping using ethnographic interviews and participatory workshops to create a weighted bipartite graph linking cultural factors to device parameters; (2) trust-enhanced biometric adaptation employing a constrained optimisation function that maximises acceptability while maintaining clinical accuracy and (3) iterative implementation with federated learning architecture for privacy-preserving feedback integration. The system was evaluated across three diverse low-income communities in Nairobi, Bangladesh and Mexico. Results: The proposed method achieves higher reliability and acceptability compared to traditional approaches, as demonstrated by preliminary results from low-resource contexts. Moreover, the framework supports modular firmware adjustments in state-of-the-art wearable devices, enabling real-time interface customisation without compromising measurement precision. Conclusion: This work bridges a critical gap in cardiovascular epidemiology by embedding cultural and trust dynamics into the core of data collection, thereby fostering equitable health monitoring in underserved communities. The significance of this approach lies in its scalability and potential to inform future biometric interventions tailored to diverse sociocultural settings.

BackgroundThe collection of self-reported physical activity using validated questionnaires has known bias and measurement error.ObjectiveAccelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control.MethodsFrom July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns.ResultsWe evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer.ConclusionsThe high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

Heterogeneous cellular and DSRC networking for Floating Car Data collection in urban areas

Studies investigating wildlife crop‐damage have been widely documented, but methods often vary and results can be difficult to compare across national and international regions. Standardized data‐collection protocols involving local communities are encouraged in wildlife management research to address this issue; however, it is not clear how data collected from such systems compare with government monitoring schemes. I established an independent monitoring program for human–elephant conflict (HEC) incidents over a 3‐year period (2008–2010) in northern Botswana, following the International Union for Conservation of Nature (IUCN) data collection protocol. In addition, the government's monitoring scheme for problem‐animal control continued in the area. With these systems running simultaneously, I compared the data collection efficiency of the 2 approaches and explored implications of each on HEC management. The total number of crop‐damage incidents recorded differed between the 2 systems. The IUCN approach was temporally more efficient than the government approach, which influenced measurements of areas damaged by elephants. Government records showed a greater mean area of damage per elephant crop‐raiding incident compared with community enumerator records. A potential challenge of using the IUCN approach for wide‐scale HEC monitoring is the selection, cost, and management of enumerators. Ultimately, both approaches were effective for general monitoring of HEC incidents when the purpose of data collection was clearly defined. Monitoring systems that address broader issues beyond providing a record of damage incidents are likely to have a greater effect in reducing human–wildlife conflicts in the long‐term. © 2017 The Wildlife Society.

The traditional roof inspection tends to be a slow and challenging task, mainly due to accessibility limitations and the risk of falls from height. In recent years, the adoption of new technologies, such as the Unmanned Aerial System (UAS) to support the inspection process has proven to be an effective option to overcome such problems. Therefore, this study aims to evaluate the use of UAS for roof inspection on post-occupation residential buildings, seeking to assist in the decision-making process. The research method adopted was the case study, structured in the following steps: (a) evaluation of items that can be viewed with UAS, (b) protocol for flight planning and data collection with UAS, (c) analysis of non-compliance checking of roof conditions, and (d) evaluation of users’ perceptions concerning the usability of UAS to support roof inspection. A total of 10 residential projects of a large construction company were inspected, totalizing 167 roofs. The analysis of the visualization potential shows that 72% of the requirements contained in the construction company’s checklist can be inspected with the UAS. Besides that, 50,5% of the non-conformities identified are related to poor execution during the construction phase, highlighting the need for improvements. According to the maintenance team’s perceptions, the UAS can contribute to increase worker safety, reduce time and effort during the data collection process, and assist in the identification of non-conformities.