Remote Connectivity Research Articles

Telemonitoring System of Greenhouses using Weather Station to calculate ANN Temperature in Crop Plant

In recent years, the use of greenhouses for agricultural production has grown rapidly and continuously in Mexico. One of the most important factors that has a deep impact in plant cultivation in order to increase production efficiency has been monitoring continuously evapotranspiration proposed in [1], which due to the topography of the country and regions designated for construction of greenhouses, should be performed remotely. To make this monitor an embedded system with a virtual machine is deployed [2]. The embedded system proposed allows remote connectivity through a telemetry and telemonitoring system via the web. To maintain optimum performance of the greenhouse should be controlled variables as temperature, humidity, solar radiation and others to achieve adapt properly controlled in the greenhouse environment, externally monitored external climatic conditions in the greenhouse with the use of an embedded weather station. In this paper the study and implementation of a specialized embedded system for greenhouses, which uses neural diffusion algorithms to obtain independently the Evapotranspiration occurs.

Recalibration and performance comparison of soil moisture sensors using regression and neural network characteristic models

In this work, laboratory based virtual instrumentation (VI) system for recalibration of various smart soil moisture sensors is implemented along with modelling and performance analysis of these sensors. This semiautomatic VI system integrates the standard gravimetric method with wireless data acquisition device to ascertain gravimetric water content in the field soil and online acquisition and analysis of the corresponding voltage samples of the sensors. The proposed VI system, is effectively used to test and record the recalibration data of commercially available analogue groove resistive sensor and capacitive (V1.0) soil moisture sensors and devise efficient characteristics models for the same. Both least-square linear-fit and polynomial-fit inverse models reveal that non-linear model of capacitive soil-moisture sensor has high accuracy (R2 = 0.98) as compared to resistive soil moisture sensor (R2 = 0.96). Further, to enhance measurement accuracy of capacitive soil moisture sensor, 3-layer neural network is modelled using back propagation algorithm to describe the output-input relationship of capacitive senor providing highest value of accuracy (R2 = 0.99). Easy to use and operate with remote connectivity, the VI tool offers ready to use on-site system for recalibration, modelling and comparison of many smart soil moisture sensors for reliable use in IoT based irrigation and water management.

Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models

During COVID-19 the new normal became an increased reliance on remote connectivity, and that fact is far away to change any time soon. The increasing number of networked devices connected to the Internet is causing an exponential growth of botnets. Subsequently, the number of DDoS (Distributed Denial of Service) attacks registered around the world also increased, especially during the pandemic lockdown. Therefore, it is crucial to understand how botnets are formed and how bots propagate within networks. In particular, analytic modelling of the botnets epidemic process is an essential component for understanding DDoS attacks, and thus mitigate their impact. In this paper, we propose two analytic epidemic models; (i) the first one for enterprise Software Define Networks (SDN) based on the SEIRS (Susceptible - Exposed - Infected - Recovered) approach, while (ii) the second model is designed for service providers’ SDN, and it is based on a novel extension of a SEIRS-SEIRS vector-borne approach. Both models illustrate how bots spread in different types of SDN networks. We found that bot infection behaves in a similar way to human epidemics, such as the novel COVID-19 outbreak. We present the calculation of the basic reproduction number $R_{\mathrm {o}}$ for both models and we test the system stability using the next generation matrix approach. We have validated the models using the final value theorem (FVT), with which we can determine the steady-state values that provide a better understanding of the propagation process.

Software Defined Networking Based on Centralized Control for Smart Grid Communications

Communication networks are growing, and there is a need for improved planning and evolution of network scaling between Smart Grid(SG) and Communication networks. Customers require capacity, speed, and connectivity services to be implemented in a shorter time frame. Vendors dictate to the Service Provider, limiting the Service Provider’s capability and their company’s technology strategy. The same applies to Smart Grid companies; scalability, reliability, and intelligence become requirements. The Smart Grid plays a significant role in providing electrical power to users and enterprises; the requirements are not phased within a location but rely on remote connectivity. The paper measures the concept of Software Defined Network to bring intelligence to a communication network and control to Smart Grid components from a centralized SDN controller. Customers and Clients assess a network on availability, reliability, control, and security. The four factors stated can be assessed on a Software Defined Network (SDN), which can be applied to an SDN Smart home, SDN Automation plant, and SDN Electrical distribution system. This capability will allow companies to perform the following services: Gain control of the network, increase security on each layer, collect data to understand the network and customer’s experience, and create services from a centralized controller. The integration of SDN+SG will develop a new target architecture to meet the advanced needs of connectivity to the end-user. Each use case directs the evolution of technology for automation and computing.

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

As mobile networks (MNs) are advancing towards meeting mobile user requirements, the rural‐urban divide still remains a major challenge. While areas within the urban space (metropolitan mobile space) are being developed, i.e., small Base Stations (BSs) empowered with computing capabilities are deployed to improve the delivery of user requirements, rural areas are left behind. Due to challenges of low population density, low income, difficult terrain, nonexistent infrastructure, and lack of power grid, remote areas have low digital penetration. This situation makes remote areas less attractive towards investments and to operate connectivity networks, thus failing to achieve universal access to the Internet. In addressing this issue, this paper proposes a new BS deployment and resource management method for remote and rural areas. Here, two MN operators share their resources towards the procurement and deployment of green energy‐powered BSs equipped with computing capabilities. Then, the network infrastructure is shared between the mobile operators, with the main goal of enabling energy‐efficient infrastructure sharing, i.e., BS and its colocated computing platform. Using this resource management strategy in rural communication sites guarantees a quality of service (QoS) comparable to that of urban communication sites. The performance evaluation conducted through simulations validates our analysis as the prediction variations observed show greater accuracy between the harvested energy and the traffic load. Also, the energy savings decrease as the number of mobile users (50 users in our case) connected to the remote site increases. Lastly, the proposed algorithm achieves 51% energy savings when compared with the 43% obtained by our benchmark algorithm. The proposed method demonstrates superior performance over the benchmark algorithm as it uses foresighted optimization where the harvested energy and the expected load are predicted over a given short‐term horizon.

RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems

Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent to the computing servers for classification. However, this approach might not be always possible because of the limited bandwidth and the privacy issues. Furthermore, it presents uncertainty in terms of latency because of the unstable remote connectivity. To support resource and delay requirements of such paradigm, joint and real-time deep co-inference framework with IoT synergy was introduced. However, scheduling the distributed, dynamic and real-time Deep Neural Network (DNN) inference requests among resource-constrained devices has not been well explored in the literature. Additionally, the distribution of DNN has drawn the attention to the privacy protection of sensitive data. In this context, various threats have been presented, including white-box attacks, where malicious devices can accurately recover received inputs if the DNN model is fully exposed to participants. In this paper, we introduce a methodology aiming at distributing the DNN tasks onto the resource-constrained devices of the IoT system, while avoiding to reveal the model to participants. We formulate such an approach as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy of the data, and the limited resources of devices. Next, due to the NP-hardness of the problem, we shape our approach as a reinforcement learning design adequate for real-time applications and highly dynamic systems, namely RL-PDNN. Our system proved its ability to outperform existing static approaches and achieve close results compared to the optimal solution.

Semantic association ability mediates the relationship between brain structure and human creativity

Creativity involves the ability to associate relatively weak or distant semantic components and combine them into novel and useful objects. Few studies have explored the brain mechanisms underlying semantic associative ability and its relationship with creativity based on semantic distance. In this study, the chain free association (CFA) task was performed, and semantic distance was quantified to measure individuals' semantic association ability, while the alternative use test (AUT) and creative activity (CAct) tasks were performed to measure creative ability. The behavioral results revealed a significant positive correlation between semantic distance and creativity. The voxel-based morphometry (VBM) analysis found the neural structural basis of semantic distance. Indeed, semantic distance was positively correlated with the gray matter volume (GMV) of the left posterior inferior temporal gyrus (LpITG), which is associated with visual word learning, semantic knowledge retrieval, and semantic memory, in addition to divergent thinking and creative traits. A mediation analysis showed semantic distance mediate the relationship between the regional GMV of LpITG and human creativity. Effectively, highly creative individuals with high regional GMV in LpITG were observed to have higher capacity of spontaneous association process. These findings shed light on the dedication of the brain areas related to remote semantic connectivity to creative thinking via individuals' spontaneous semantic association ability.

Abnormal blood oxygen level-dependent fluctuations and remote connectivity in sleep-related hypermotor epilepsy.

Sleep-related hypermotor epilepsy (SHE) is a form of the epileptic syndrome that involves stereotyped hypermotor seizures and presents as asymmetric tonic or dystonic posturing events. We aimed to investigate the brain activities of SHE patients using structural and functional magnetic resonance imaging (fMRI). A total of 41 patients with SHE and 41 age- and sex-matched healthy controls (HCs) were prospectively enrolled and assessed using fMRI. The two groups were compared in amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), and potential correlations between these measures and clinical features were also examined. The involvement of functional network integration was explored by analyzing seed-based functional connectivity. In SHE patients, ALFF in the right precentral gyrus was significantly higher than in HCs, and ReHo in the left postcentral and right precentral gyrus was higher. None of the brain regions had lower ALFF or ReHo compared to HCs. ReHo in the left postcentral gyrus and ALFF in the right precentral gyrus were both negatively correlated with epilepsy duration. Patients with SHE had higher functional connectivity mainly in the precuneus, postcentral gyrus, and supplementary motor area. However, none of the brain regions in SHE group presented lower functional connectivity than in HCs. SHE is associated with disrupted regional and interregional functional activities. The patients showed abnormalities within the sensorimotor gyrus and supplementary motor area, suggesting spontaneous fluctuations correlated with remote functional brain network. These results at the whole-brain level argue for further investigation into connectivity disturbance in SHE.

Rapid Assessment of Coagulation at the Point of Care With the Hemochron Signature Elite System

Real-time point-of-care (POC) measurement of coagulation parameters, including activated clotting time (ACT), activated partial thromboplastin time, and prothrombin time with calculated international normalized ratio can improve patient outcomes in clinical settings, such as surgical and minimally invasive cardiac procedures, critical care units, and emergency departments. Point-of-care analyzers used in these settings must be small, portable, robust, and easy to use. The Hemochron Signature Elite system (Instrumentation Laboratory) offers 6 types of single-use disposable coagulation test cartridges, ACT+ (to measure high to moderate unfractionated heparin concentrations), ACT-LR (to measure moderate to low unfractionated heparin concentrations), activated partial thromboplastin time (citrate or whole-blood samples), and prothrombin time/international normalized ratio (citrate or whole-blood samples). Using a mechanical clot-detection technology, 1 to 2 drops of blood are mixed with an assay-specific reagent. Results are analyzed, displayed, and archived within minutes of testing. An integral barcode reader scans patient and operator IDs, and reagent lot details for documentation and traceability. The system supports remote connectivity (POCT01-A–compliant) and POC management of data, operators, and analyzers with GEMweb Plus 500 Custom Connectivity solution (Instrumentation Laboratory). The Hemochron Signature Elite system provides rapid and reliable testing with the broadest coagulation test menu of any POC instrument.

Altered Brain Function in Cerebral Small Vessel Disease Patients With Gait Disorders: A Resting-State Functional MRI Study

Gait disturbances are important clinical features of cerebral small vessel disease (CSVD) that increase the risk of falls and disability. Brain structural alterations and gait disturbances in CSVD patients have been well demonstrated. However, intrinsic resting cerebral function patterns in CSVD patients with gait disorders remain largely unknown. Fifty-eight CSVD patients were enrolled in our studies and categorized into the gait disorder group (CSVD-GD, n = 29) and no-gait disorder group (CSVD-NGD, n = 29) based on a gait examination. Gait was quantitatively assessed with the Timed Up and Go test and the intelligent device for energy expenditure and activity (IDEEA). Functional MRI and fractional amplitude of low-frequency fluctuation (fALFF) analyses were employed to explore local intrinsic neural oscillation alterations. Functional connectivity based on fALFF results was calculated to detect the potential changes in remote connectivity. Compared with the CSVD-NGD group, the CSVD-GD group showed decreased fALFF in regions mainly located in the sensorimotor network and frontoparietal network, such as the left supplementary motor area (SMA.L) and the left superior parietal gyrus, and increased fALFF in the right inferior frontal gyrus (orbital part), the left caudate, and the left precuneus. Moreover, the CSVD-GD patients exhibited lower connectivity between the SMA.L and temporal lobe, which was related to gait speed. The fALFF value of the SMA.L was associated with cadence. This study highlights the regional and network interaction abnormalities of the SMA in CSVD patients with gait disturbances. These findings could provide further insight into the neural mechanisms of gait disturbances in CSVD.

The Application of Video Conferencing in Judicial Proceedings in Nigeria.

The development of Video Conferencing Technology has led to significant improvements in legal, institutional and business practices all over the world. Businesses, educators, healthcare providers, governments, and many other institutions have been able to adopt video conferencing tools as an alternative to travel and less comprehensive forms of remote connectivity. This article will thus examine the concept and use of video conferencing and its application to Nigerian judicial proceeding. It will also examine the legal framework on Video Conferencing in Nigeria judicial system.

Remote Access Communications Security: Analysis of User Authentication Roles in Organizations

Remote access is a means of accessing resources outside one’s immediate physical location. This has made employee mobility more effective and productive for most organizations. Remote access can be achieved via various channels of remote communication, the most common being Virtual Private Networks (VPNs). The demand for remote access is on the rise, especially during the Covid-19 pandemic, and will continue to increase as most organizations are re-structuring to make telecommuting a permanent part of their mode of operation. Employee mobility, while presenting organizations with some advantages, comes with the associated risk of exposing corporate cyber assets to attackers. The remote user and the remote connectivity technology present some vulnerabilities which can be exploited by any threat agent to violate the confidentiality, integrity and availability (CIA) dimensions of these cyber assets. So, how are users and remote devices authenticated? To what extent is the established connection secured? With employee mobility on the rise, it is necessary to analyze the user authentication role since the mobile employee is not under the monitoring radar of the organization, and the environment from which the mobile employee connects may be vulnerable. In this study, an experiment was setup to ascertain the user authentication roles. The experiment showed the process of 2FA in user authentication and it proved to be an effective means of improving user authentication during remote access. This was depicted via the use of what the user has (mobile phone/soft-token) as a second factor in addition to what the user knows, i.e. password. This authentication method overcomes the security weaknesses inherent in single-factor user authentication via the use of password only. However, the results also showed that though 2FA user authentication ensures security, the remote devices could exhibit further vulnerabilities and pose serious risks to the organization. Thus, a varied implementation was recommended to further enhance the security of remote access communication with regards to the remote user authentication.

Focus on software, data acquisition, and instrumentation

Focus on software, data acquisition, and instrumentation

Functional connectivity within lateral posterior parietal cortex in moderate to severe traumatic brain injury.

Functional brain networks converge on areas of heteromodal processing such as lateral posterior parietal cortex (PPC). Traumatic brain injury (TBI) alters global connectivity patterns secondary to both focal and diffuse damage, but little is known about how it impacts regional environments. We examined local PPC functioning in individuals with moderate-severe TBI and controls during resting-state functional magnetic resonance imaging (rs-fMRI). Eighteen individuals with moderate-severe TBI and 19 healthy controls underwent rs-fMRI and neurocognitive testing. Seed-based analyses characterized remote connectivity of PPC subregions. Voxelwise graph theoretical approaches were used to probe local PPC connectivity and modularity within and between groups, and to examine relationships between local functioning and cognition. Seed-based findings included increased connectivity from left and right hemispheric subregions to right-lateralized default mode and frontoparietal control networks in TBI compared to controls. Graph theoretical analyses revealed increased connection strength within right PPC relative to the contralateral region in TBI. Across groups, right PPC also showed decreased betweenness centrality compared with left PPC. Groups did not differ in the extent of modularity within left or right PPC, but there was less interindividual variability in modular structure within the TBI group. Right PPC modularity significantly predicted individual differences in cognitive performance. Our findings substantiate hyperconnectivity on both local and global levels after TBI and propose a special role for local right hemispheric functioning in supporting cognition independent of neurologic status. Hyperconnectivity does not appear to result from breakdown in local modular organization and may reflect shared responses to neurologic disruption among those with TBI. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment.

This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment wear before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry.

A multivariant secure framework for smart mobile health application

AbstractWireless sensor network enables remote connectivity of technological devices such as smart mobile with the internet. Due to its low cost as well as easy availability of data sharing and accessing devices, the Internet of Things (IoT) has grown exponentially during the past few years. The availability of these devices plays a remarkable role in the new era of mHealth. In mHealth, the sensors generate enormous amounts of data and the context‐aware computing has proven to collect and manage the data. The context aware computing is a new domain to be aware of context of involved devices. The context‐aware computing is playing a very significant part in the development of smart mobile health applications to monitor the health of patients more efficiently. Security is one of the key challenges in IoT‐based mHealth application development. The wireless nature of IoT devices motivates attackers to attack on application; these vulnerable attacks can be denial of service attack, sinkhole attack, and select forwarding attack. These attacks lead intruders to disrupt the application's functionality, data packet drops to malicious end and changes the route of data and forwards the data packet to other location. There is a need to timely detect and prevent these threats in mobile health applications. Existing work includes many security frameworks to secure the mobile health applications but all have some drawbacks. This paper presents existing frameworks, the impact of threats on applications, on information, and different security levels. From this line of research, we propose a security framework with two algorithms, ie, (i) patient priority autonomous call and (ii) location distance based switch, for mobile health applications and make a comparative analysis of the proposed framework with the existing ones.

Disruption of resting-state functional connectivity of right posterior insula in adolescents and young adults with major depressive disorder

BackgroundThe neural basis of Major Depressive Disorder (MDD) which is a clinical syndrome characterized by emotional and cognitive impairments is poorly understood. Accumulating evidence has suggested that the insula is an important substrate underlying the mechanism of MDD. This study aimed to examine the disrupted resting-state brain regional function in insula and to further investigate the associated resting-state functional connectivity (rs-FC) of insula underlie the MDD in adolescents and young adults. MethodsWe employed 3.0T resting-state functional magnetic resonance imaging (rs-fMRI) to acquire data from 76 adolescents and young adults with MDD and 44 age and sex matched healthy control subjects. We employed a regional Amplitude of Low-Frequency Fluctuation (ALFF) analysis to explore local intrinsic neural oscillation alterations in insula and an ALFF-based functional connectivity (FC) approach to detect the potential changes in remote connectivity with insula in adolescents and young adults with MDD. ResultsBy applying ALFF analysis, significantly decreased activities were detected in bilateral insula, and in particular in right anterior insular gyrus (MNI; ROI1: 42, 24, −3), right posterior insular gyrus (Montreal Neurological Institute, MNI; ROI2: 36, −9, 15) and left anterior insular gyrus (MNI; ROI3: −36, 12, 9) in patients with MDD compared to the healthy controls (p < 0.05, 1000 permutations, TFCE corrected). With ROI2 as the seed in the subsequent ALFF-based rs-FC analysis, patients with MDD were observed to have significantly reduced FC with bilateral middle occipital gyrus, lingual gyrus, calcarine, postcentral gyrus, precentral gyrus, supramarginal area, superior temporal gyrus and middle cingulate gyrus as compared to the healthy controls (p < 0.05, 1000 permutations, TFCE corrected). No significant differences of FC were detected between the patients and healthy controls when using ROI1 and ROI3 as the seeds. We found no correlations between ALFF or rs-FC values and the severity of depression as estimated by Hamilton Depression Rating Scale (HAM-D). LimitationsClinical information were limited and no significant correlations were found between imaging variables and HAM-D scores, which reduces the power to interpret the present findings. A cross-sectional design was employed in this study so that it is not possible to know whether the abnormal ALFF or altered brain FC of insula reflects a state or trait effect in young people with MDD. ConclusionsThis study highlights the regional/network interaction abnormalities of insula in adolescents and young adults with MDD, and could provide further insight into understanding the neural pathomechanism of MDD in young patients.

Abnormal functional network centrality in drug-naïve boys with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood and is characterized by inattention, impulsivity, and hyperactivity. Observations of distributed functional abnormalities in ADHD suggest aberrant large-scale brain network connectivity. However, few studies have measured the voxel-wise network centrality of boys with ADHD, which captures the functional relationships of a given voxel within the entire connectivity matrix of the brain. Here, to examine the network patterns characterizing children with ADHD, we recruited 47 boys with ADHD and 21 matched control boys who underwent resting-state functional imaging scanning in a 3.0 T MRI unit. We measured voxel-wise network centrality, indexing local functional relationships across the entire brain connectome, termed degree centrality (DC). Then, we chose the brain regions with altered DC as seeds to examine the remote functional connectivity (FC) of brain regions. We found that boys with ADHD exhibited (1) decreased centrality in the left superior temporal gyrus (STG) and increased centrality in the left superior occipital lobe (SOL) and right inferior parietal lobe(IPL); (2) decreased FC between the STG and the putamen and thalamus, which belong to the cognitive cortico-striatal-thalamic-cortical (CSTC) loop, and increased FC between the STG and medial/superior frontal gyrus within the affective CSTC loop; and (3) decreased connectivity between the SOL and cuneus within the dorsal attention network. Our results demonstrated that patients with ADHD show a connectivity-based pathophysiological process in the cognitive and affective CSTC loops and attention network.

Packet Level Performance Assessment of mmWave Backhauling Technology for 3GPP NR Systems

Recently standardized millimeter-wave (mmWave) band 3GPP New Radio systems are expected to bring extraordinary rates to the air interface efficiently providing commercial-grade enhanced mobile broadband services in hotspot areas. One of the challenges of such systems is efficient offloading of the data from access points (AP) to the network infrastructure. This task is of special importance for APs installed in remote areas with no transport network available. In this paper, we assess the packet level performance of mmWave technology for cost-efficient backhauling of remote 3GPP NR APs connectivity “islands”. Using a queuing system with arrival processes of the same priority competing for transmission resources, we assess the aggregated and per-AP packet loss probability as a function environmental conditions, mmWave system specifics, and generated traffic volume. We show that the autocorrelation in aggregated traffic provides a significant impact on service characteristics of mmWave backhaul and needs to be compensated by increasing either emitted power or the number of antenna array elements. The effect of autocorrelation in the per-AP traffic and background traffic from other APs also negatively affects the per-AP packet loss probability. However, the effect is of different magnitude and heavily depends on the load fraction of per-AP traffic in the aggregated traffic stream. The developed model can be used to parameterize mmWave backhaul links as a function of the propagation environment, system design, and traffic conditions.

Influences of the Kuroshio on Interisland Remote Connectivity of Corals Across the Nansei Archipelago in the East China Sea

AbstractFor the preservation and protection of coral habitats along the Nansei Archipelago in the East China Sea, a submesoscale eddy‐resolving synoptic ocean model was developed based on the Regional Oceanic Modeling System coupled with a 3‐D Lagrangian particle tracking model. Millions of neutrally buoyant particles representing coral spawn and larvae were released from 19 major islands and one lagoon every spring from 2012 to 2015. The model results were compared to satellite data, in situ observation, and surface drifters to confirm reasonable agreement. The connectivity matrix across the archipelago was quantified using Lagrangian probability density functions of the modeled particle displacement. Most particles remained near the release areas, while some traveled long distances by the northeastward drifting Kuroshio, leading to notable interisland coral transport across the archipelago that promotes interisland connectivity. A possible mechanism was examined by analyzing the transition from coastal to pelagic transport of the particles released from the Yaeyama Islands, the southernmost area of the archipelago. The Kuroshio trapped the particles released from the northern coast of the islands with considerable temporal variability in the entrainment rate. By contrast, particles released from the southern coast are markedly affected by the eastward current around the release sites, which significantly reduces their entrainment in the Kuroshio and, thus, long‐distance transport. Some entrained particles were expelled abruptly from the Kuroshio, trapped by the southwestward drifting Kuroshio Counter Current developed between the Kuroshio and the archipelago, and subsequently transported eastward to the islands.