Opportunistic Data Collection Research Articles

Beyond 5G Satellite Transmission and Indoor UAVs in Dams Monitoring: An Opportunistic Data Collection Strategy for Isolated Rural Areas

Beyond 5G Satellite Transmission and Indoor UAVs in Dams Monitoring: An Opportunistic Data Collection Strategy for Isolated Rural Areas

Leading the way – presence of brown bear <i>Ursus arctos</i>, lynx <i>Lynx lynx</i> and grey wolf <i>Canis lupus</i> underlines the integrity and corridor function of the upper Neretva Valley in Bosnia and Herzegovina

Despite its connecting character to the Sutjeska National Park in the North-East, the course of the upper Neretva (Ulog and Borač valley) in Bosnia and Herzegovina (BiH), has to date not been an area of focus for carnivore research. With the aim of contributing to an improved understanding of the present distribution of large and endangered carnivores in this area, a field survey was conducted during the Neretva Science Week 2022. Opportunistic data collection was carried out along a predefined road transect (20 km) and six line transects (13 km) tracked by foot. Additionally, reports of mammal signs for six corresponding research sites along the Neretva were registered. Altogether, at least 15 mammal species could be distinguished. From the identified carnivores, four are listed in Annex II of the Habitats Directive. Furthermore, the signs of lynx presence, in addition to brown bear and grey wolf, highlight the connectivity value of the upper Neretva in its current state. Based on information from local practitioners, the main threats to large carnivores are hunting pressure and a negative perception as competitors. A likely decline in habitat quality and fragmentation, due to the planned hydropower plants expansion and logging activities, exacerbate these threats. Consequently, further systematic monitoring is recommended to assess the possible impact of human driven environmental changes.

Deep Learning–Based Autonomous Road Condition Assessment Leveraging Inexpensive RGB and Depth Sensors and Heterogeneous Data Fusion: Pothole Detection and Quantification

Poor condition of roads is a major factor for traffic accidents and damage to vehicles. A significant portion of car accidents is attributed to severe three-dimensional (3D) pavement distresses such as potholes, ruttings, and ravelings. Insufficient road condition assessment is responsible for the poor condition of roads. To inspect the condition of the pavement surfaces more frequently and efficiently, an inexpensive data acquisition system was developed that consists of a consumer-grade RGB-D sensor and an edge computing device that can be mounted on vehicles and collect data while driving vehicles. The RGB-D sensor is used for collecting two-dimensional (2D) color images and corresponding 3D depth data, and the lightweight edge computing device is used to control the RGB-D sensor and store the collected data. An RGB-D pavement surface data set is generated. Furthermore, encoder-decoder deep convolutional neural networks (DCNNs) consisting of one or two encoders, and one decoder trained on heterogeneous RGB-D pavement surface data are used for pothole segmentation. Comprehensive experiments using different depth encoding techniques and data fusion methods including data- and feature-level fusion were performed to investigate the efficacy of defect detection using DCNNs. Experimental results demonstrate that the feature-level RGB-D data fusion based on the surface normal encoding of depth data outperform other approaches in terms of segmentation accuracy, where the mean intersection over union (IoU) over 10-fold cross-validation of 0.82 is achieved that shows a 7.7% improvement compared with a network trained only on RGB data. In addition, this study explores the efficacy of indirectly using depth information for pothole detection when depth data are not available. Additionally, the semantic segmentation results were utilized to quantify the severity level of the potholes assisting in maintenance decision-making. The result from these comprehensive experiments using an RGB-D pavement surface data set gathered through the proposed data acquisition system is a stepping stone for opportunistic data collection and processing through crowdsourcing and Internet of Things in future smart cities for effective road assessment. Finally, suggestions about the improvement of the proposed system are discussed.

QACM: Quality Aware Crowd Sensing in Mobile Computing

Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people’s mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.

Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.

Internet of things based smart city design using fog computing and fuzzy logic

• “Internet of Things based Smart City Design using Fog Computing and Fuzzy Logic”. • An edge computing paradigm using human interactions for their decision-making process. • Employs probabilistic history-based parameters that keep track of node interactions. • Context-aware smart paradigm considering predictability ratios. • A nature-inspired algorithm used to make the routing considerations. • A fuzzy rule-based system using power-buffer capacity-based index to make classes. The prevalence of IoT based technology in contemporary society allows for the planning of smart city infrastructure with individually owned devices as participating nodes in a social smart network. Edge computing application into such an IoT network facilitates the distributed computing framework by bringing the computational application closer to the end-users in form of Fog sub-networks, comprising the local participating end-user IoT nodes as service requesters and specialized servers catering to the requests, modelled for the infrastructure category they are delivering (Smart healthcare, ITS, Smart architecture, etc.). Opportunistic-based last-mile connectivity between the Fog servers and participating nodes aides the dynamicity of the topology for efficient information. Aiding the same, the proposed edge computing enabled, sustainable, opportunistic IoT-based intuitive data exchange, collection and operation scheme that employs a dynamic routing procedure that considers human interactions, their movement patterns and their decision-making process. The proposed scheme employs probabilistic history-based parameters that keep track of node interactions. The interactions are modelled on the Firefly PSO, allowing exploitation of the social mobility. The Fuzzy logic is used for hop decision making based on the social and attribute-based parameters which mimic intuitive and pragmatic decision making of humans in terms of social interactions and mobility.

An Opportunistic Assessment of the Impact of Squirrelpox Disease Outbreaks upon a Red Squirrel Population Sympatric with Grey Squirrels in Wales.

Simple SummaryIn Europe, squirrelpox virus is carried by non-native grey squirrels and spread into native red squirrel populations. The virus causes a large proportion of infected red squirrels to die and contributes to local declines and the replacement by grey squirrels. There are relatively few published studies quantifying the impact of disease amongst red squirrels. We present findings from a short-term study in north Wales, United Kingdom.Native red squirrels (Sciurus vulgaris) persisted in the coastal mainland woodlands of northern Gwynedd whilst sympatric with an invasive grey squirrel (Sciurus carolinensis) population suppressed by culling. Squirrelpox disease in the red squirrel population was recorded in 2017 and 2020/21. An autumn 2020 outbreak was associated with only 17.4% of animals caught and marked in the preceding June known to be present in March 2021. Despite an opportunistic data collection lacking the rigour of empirical experimental design, we observed low local survival rates similar to previously published accounts reported during major squirrelpox outbreaks. The use of a conservation dog to detect red squirrel carcasses resulted in positive detection and confirmation of a temporal and spatial expansion of one disease outbreak. The study is the first in Wales to use conservation dogs and the findings reinforce the vital strategic importance of geographical isolation reducing sympatry of red with grey squirrels in European regions where the introduced congener is a source of the squirrelpox infection.

Observations on the use of a pain numbing device for repetitive percutaneous sampling in sheep.

To evaluate the success of a commercially available analgesic device (CoolSense; Coolsense Ltd, Tel Aviv, Israel) in ameliorating pain while sampling from subcutaneous tissue cages in sheep. The CoolSense device was used as part of a major parent study involving repetitive percutaneous sampling of subcutaneous tissue cages in seven sheep. Sampling was performed by passing a hypodermic needle through the skin and withdrawing fluid from the tissue cage. Each sheep had 10 tissue cages that were individually sampled 14 times over 74 h. The device was placed on the skin of the sampling site immediately before sampling cooling and numbing the skin. The reaction of the sheep was observed by the operators, flinching or jumping as the needle was passed through the skin was deemed to be a failure. We recorded the success or failure of the device for each needle stick. This was opportunistic data collection as part of a pharmacokinetic trial, therefore no controls were included. A total of 1655 observations were recorded and then analysed using a generalised linear mixed model. Overall, 1380 of 1655 (83.4%) observations were recorded as successfully providing analgesia. Marked inter-occasion variability was noted with success ranging from 61.42% to 92.86% across sheep:period (approximately 140 observations each). As no controls were available, the effect of treatment could not be evaluated. The CoolSense device is a viable option for veterinary research and clinical applications.

Drivers of spatio-temporal variation in mosquito submissions to the citizen science project \u2018M\xfcckenatlas\u2019

Intensified travel activities of humans and the ever growing global trade create opportunities of arthropod-borne disease agents and their vectors, such as mosquitoes, to establish in new regions. To update the knowledge of mosquito occurrence and distribution, a national mosquito monitoring programme was initiated in Germany in 2011, which has been complemented by a citizen science project, the ‘Mückenatlas’ since 2012. We analysed the ‘Mückenatlas’ dataset to (1) investigate causes of variation in submission numbers from the start of the project until 2017 and to (2) reveal biases induced by opportunistic data collection. Our results show that the temporal variation of submissions over the years is driven by fluctuating topicality of mosquito-borne diseases in the media and large-scale climate conditions. Hurdle models suggest a positive association of submission numbers with human population, catch location in the former political East Germany and the presence of water bodies, whereas precipitation and wind speed are negative predictors. We conclude that most anthropogenic and environmental effects on submission patterns are associated with the participants’ (recording) behaviour. Understanding how the citizen scientists’ behaviour shape opportunistic datasets help to take full advantage of the available information.

Fishing Gear as a Data Collection Platform: Opportunities to Fill Spatial and Temporal Gaps in Operational Sub-Surface Observation Networks

While the open oceans are well covered by automated ocean measurement instruments, there is a lack of sub-surface oceanographic observation in coastal and shelf seas. Commercial fishing gear such as bottom trawls, pots, traps and long lines can act as a platform for sensors, which collect physical oceanographic data concurrently with normal fishing operations. The lack of observed in-situ ocean data in coastal and shelf seas limits operational oceanography, weather forecasting, maritime industries, and climate change monitoring. In addition, using fishing gear as an ocean observation platform has auxiliary benefits for fisheries management including stakeholder involvement. This study quantifies and compares the existing sub-surface in-situ data coverage with the spatial distribution of fishing activities. The results show that integration with fishing could contribute to filling some of the most pressing gaps in existing ocean observation systems in coastal and shelf seas. There are limitations inherent to opportunistic data collection, mainly related to spatial and temporal heterogeneity of fishing activities. However, we make the case that fishery-based observations have the potential to complement existing ocean observing systems in areas where oceanographic data are lacking and needed most in order to ensure long term sustainability of oceanographic monitoring.

Pushing the Limits of Transmission Concurrency for Low Power Wireless Networks

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

Opportunistic Data Collection in Cognitive Wireless Sensor Networks: Air–Ground Collaborative Online Planning

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air–ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air–ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

Opportunistic UAV Utilization in Wireless Networks: Motivations, Applications, and Challenges

With the prominent advancement of flight control and intelligent transportation technology, UAVs will play an important role in air traffic. Besides being deployed as dedicated aerial communication platforms, a large proportion of UAVs will be operated by different companies with various flight missions. In the existing literature, such UAVs are usually treated as consumers of spectrum resources. However, they may also bring opportunities of air-ground line-of-sight and relay links, which can improve the transmission optimization of ground networks. This article explores the opportunistic assistance of such UAVs for ground networks from a new perspective, called OUU. Various opportunistic transmission models and corresponding application scenarios are introduced according to different flight modes of UAVs, including opportunistic data dissemination, collection, caching, computing, and forwarding. Two preliminary cases demonstrate that effective OUU models can improve network performance without relying on dedicated deployment of aerial communication platforms, and thus alleviate the aerial traffic congestion issue. After discussing the challenges brought by large-scale and highly dynamic UAV networks, this article further enumerates the promising research directions and related optimization frameworks for the OUU model.

Structural and functional multi-platform MRI series of a single human volunteer over more than fifteen years

We present MRI data from a single human volunteer consisting in over 599 multi-contrast MR images (T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery, T2* gradient-echo, diffusion, susceptibility-weighted, arterial-spin labelled, and resting state BOLD functional connectivity imaging) acquired in over 73 sessions on 36 different scanners (13 models, three manufacturers) over the course of 15+ years (cf. Data records). Data included planned data collection acquired within the Consortium pour l’identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. These multiple within- and between-centre scans over a substantial time course of a single, cognitively healthy volunteer can be useful to answer a number of methodological questions of interest to the community.

The Canadian Dementia Imaging Protocol: Harmonization validity for morphometry measurements.

The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies.

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass‐participation internet‐based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high‐quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state‐of‐the‐art analytical methods, and well‐supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

The Chirocopter: A UAV for recording sound and video of bats at altitude

Abstract Most recordings of bats are conducted with fixed equipment, which relies on opportunistic data collection. Unmanned aerial vehicles (UAV; such as drones) are considered inappropriate for recording bats due to ultrasound noise constraints. We developed a UAV system that physically isolates UAV noise, so we can record, with 3D manoeuvrability, ultrasonic audio and spatial thermal data of bat flight at altitude. We tested the noise of our UAV with various payloads and microphone configurations to characterize the ultrasonic noise of our system, physically isolate drone noise from the microphone, and maximize UAV flight performance. Over 84 min of recordings, we captured 3,847 echolocation signals from bats with corresponding thermal data of bat flight. Our system provides a feasible mechanism to capture both acoustic and video data of bats aloft at flexible locations and altitudes. We include information on how to extend our method to apply to acoustic recordings in the audible (20 Hz–20 kHz) range for recording sounds of other taxa.

Bayesian estimation of species relative abundances and habitat preferences using opportunistic data

We develop a new statistical procedure to monitor relative species abundances and their respective preferences for different habitat types, using opportunistic data. Following Giraud et al. (Biometrics 72(2):649–658, 2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Species observations are modeled by Poisson distributions whose parameters quantify species abundances and habitat preferences, and are estimated using Bayesian computations. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France).

Trade of parrots in urban areas of Madagascar

The live capture of parrots is causing increasing concern across Africa. In Madagascar, home to three species of parrot ( Coracopsis nigra , C. vasa , A gapornis canus ), no study has examined how these species are being extracted from the wild and traded. In this study, we examined the procurement, length of ownership, and the end of ownership of pet parrots. Data were collected via household surveys (n = 440 interviews in 9 towns), market visits (nd = 1 7 markets in 6 towns), and opportunistic data collection methods in urban, Malagasy towns. Most Coracopsis spp. are purchased (59%) or captured directly by the owner from the wild (22%), although we were unable to determine how A. canus was procured. Survey respondents reported purchasing Coracopsis spp. for the price of USD 5.36 ± 3.20. The average Coracopsis spp. was kept in captivity for 3.1 7 ± 2.51 years. No survey respondents provided information on the purchase price or length of ownership for A. canus . Ownership ended primarily when Coracopsis spp. escaped/flew away (36%) or died of unknown causes (21%). A. canus also flew away, although this was only reported in one instance. In-country demand appears to be met by a trade network of both informal and formal actors. It is unclear whether current protections for Madagascar’s parrots, as far as the domestic market is concerned, are sufficient to ensure sustainable extraction of live individuals.

Multi-layer-based opportunistic data collection in mobile crowdsourcing networks

Along with the explosive popularity of wireless mobile devices and availability of high data rates, new crowdsourcing paradigms have emerged to leverage the power of problem-solving by crowds. A crucial challenge in crowdsourcing is data collection. With the increasing number of mobile users, device to device communication with opportunistic connections has become a real possibility, reducing the load on infrastructure based networks. Crowdsourcing over such opportunistic links presents with unique challenges. This paper proposes to exploit opportunistic transmission to collect data in crowdsourced networks, by using multiple layers of social graphs along with weight training for energy efficient data collection. We design two types of multi-layer-based opportunistic data collection methods by using different dimensions of data. Simulation experiments show that using these techniques, delivery ratio can be increased while reducing the load and energy consumption of the mobile network.