CDCP: Collaborative Data Collection Protocol in Vehicular Sensor Networks

Selective data collection in vehicular networks for traffic control applications

YAP is a protocol designed to efficiently and robustly relay network configuration states of nodes to a designated node that collects and stores the configuration states of all the nodes. Once the configuration states from the nodes are collected, any application can query the information to perform its own specific function. For example, a GUI application can query the database to display the logical topology of the networks. Also a Configuration Manager can query the database to decide how to reconfigure the network. We describe how YAP was integrated with protocols designed to auto-configure IP-related attributes of nodes (using DRCP and DCDP).

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

In complex marine environments, underwater sensor nodes exhibit continuous random drift driven by ocean currents, characterized by spatiotemporal correlations. This poses severe challenges, such as link interruptions and scheduling conflicts, for traditional AUV data collection protocols that assume static nodes or simple mobility models. To overcome the performance degradation caused by position uncertainty, this paper proposes a Robust Data Collection and Adaptive Scheduling MAC protocol (RDTAS-MAC). The protocol innovatively constructs a node drift model based on the Ornstein-Uhlenbeck (OU) process, which not only captures the mean-reverting characteristics of perturbations but also provides accurate prior distributions for subsequent state prediction. On this basis, the selection of the AUV's hovering position is modeled as a chance-constrained programming problem. A two-stage Monte Carlo search algorithm is designed to maximize the expected data collection utility while strictly guaranteeing probabilistic connectivity. Furthermore, addressing the random jitter in propagation delay, an uncertainty-aware scheduling mechanism is proposed. By dynamically mapping the error covariance of position estimates to time-domain guard intervals, collision-free parallel data transmission is achieved. Experimental results verify that the RDTAS-MAC protocol significantly improves throughput and reduces packet loss rates in strong perturbation scenarios. This study validates the necessity of incorporating physical layer position uncertainty into MAC layer design, providing a highly reliable solution for dynamic underwater acoustic networks.

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.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

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.

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.

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.

In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads' throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

With the adoption of sensing technology, vehicles are able to gather information about driver's environment, like speed, temperature, moisture and road condition, etc. The state-of-the-art wireless technologies also provide vehicles with the ability of communicating to their neighboring vehicles on the road, as well as the static frameworks on the road side. Vehicular Sensor Network (VSN) is an emerging paradigm in vehicular networks. In this paper, we propose a new VSN scenario, Road Probing. The Road Side Unit (RSU) initiates the probing process and selects the passing by vehicles as probes to collect traffic and environment information. The selected vehicles sense the desired data and forward it back to the RSU in a multi-hop fashion. A dynamically growing one-dimension probing chain originated from the RSU is formed by participated vehicles as the probing process going on. A mobility profile based probe selection method is developed to improve this chain's connectivity as it's the key issue to assure the quality of data collection. The performance of Road Probing is demonstrated by simulation experiments.

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.

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).

HashQuery, a Hash-area-based data dissemination protocol, was designed in wireless sensor networks. Using a Hash function which uses time as the key, both mobile sinks and sensors can determine the same Hash area. The sensors can send the information about the events that they monitor to the Hash area and the mobile sinks need only to query that area instead of flooding among the whole network, and thus much energy can be saved. In addition, the location of the Hash area changes over time so as to balance the energy consumption in the whole network. Theoretical analysis shows that the proposed protocol can be energy-efficient and simulation studies further show that when there are 5 sources and 5 sinks in the network, it can save at least 50% energy compared with the existing two-tier data dissemination(TTDD) protocol, especially in large-scale wireless sensor networks.

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