Search

Study region: South Korea Study focus: To overcome the limitations of relying solely on ground precipitation gauges, this study utilizes radar precipitation data to investigate the temporal scaling behavior of extreme rainfall values. Maximum precipitation and extreme quantile values for each grid point were calculated using the rolling-window summation method. The investigation focuses on the relationship between maximum precipitation and duration. New hydrological insights for the region: The study's findings are as follows: (1) Radar estimates higher maximum precipitation values than ground gauges, especially in mountainous areas with sparse gauge coverage; (2) The maximum precipitation-duration relationship deviates from a power-law relationship primarily due to unusual short-duration extreme rainfall events; (3) Lower-quantile high rainfall values show a stronger power-law relationship influenced by various rainfall mechanisms; (4) The East Asian rainy season induces greater extreme rainfall for durations up to 6 h, while longer durations are dominated by typhoons, indicating different flood risks; (5) Maximum precipitation values for most durations are observed on Jeju Island, primarily caused by typhoon events. Considering that climate change is expected to induce a northward shift in typhoon paths, appropriate flood defense measures should be implemented, especially in the southern part of the Korean Peninsula. These findings highlight how different observation methods can significantly impact flood risk assessment and the design of key hydraulic structures.

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) system is a state-of-the-art emerging technology targeted towards beyond fifth-generation (B5G) and sixth-generation (6G) communication networks. This network pertains to a dense deployment of access points (APs) dispersed over a large geographical area to serve a small number of users at the same frequency and time resources. The CF-mMIMO architecture offers resilient connectivity, interference management, power efficiency, high throughput, and macrodiversity. Moreover, this communication technique eliminates cell boundaries and facilitates the users by introducing overlapping regions, thus providing consistent quality of service (QoS) throughout the region. However, the complexity of CF-mMIMO systems increases considerably when numerous APs are dispersed over a large geographical area. Therefore, several studies have been carried out to determine the optimal solution with minimum complexity of the CF-mMIMO system. Herein, a thorough investigation of the literature on the CF-mMIMO system is presented, considering all aspects from architecture to applications. The study provides a detailed survey of CF-mMIMO architecture, fronthaul, and backhaul, as well as the challenges associated with them; deployment methodologies and challenges for practical implementation of CF-mMIMO systems are also discussed. Furthermore, we reviewed the impact of transmitter and receiver antennae on the capacity of CF-mMIMO enabled with millimeter wave (mmWave). The numerical findings indicate that the higher degree of freedom required for spatial multiplexing allows multiantenna users to surpass single-antenna users in terms of capacity. This study holds significance owing to the thorough examination of the CF-mMIMO system model, channel estimation, scalability problems, working algorithms, communication protocol, deep learning-based solutions, linkage to B5G and 6G, and key challenges. Moreover, this study presents a detailed discussion and research survey on the system model, deployment issues, deep learning, and potential applications of the CF-mMIMO system.

Network Slicing (NS) was proposed as a viable solution in Release 15 of Third Generation Partnership Project (3GPP) to allocate the limited resources among different service types for improving their Quality-of-Service (QoS). However, the advanced vehicular applications such as autonomous driving, platooning, remote driving, etc. have stringent QoS demands and the standard NS architecture is not sustainable for these services. Therefore, we propose a solution compatible with the standard 3GPP NS architecture that implements an Actor-Critic based Deep Reinforcement Learning (DRL) algorithm in the Network Slice Subnet Management Function (NSSMF). The algorithm allocates and manages the limited resources among different slices based on their real-time traffic demands. We generate real-time traffic for each service type and train the algorithm to improve the QoS of each service type in the network. The proposed method is evaluated for the training performance of the proposed algorithm as well as the Service level agreement Satisfaction Ratio (SSR) of each slice. The results exhibit that the proposed method not only improves SSR of each slice, but also performs well in case of increased node density in the network.

Abstract Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

Predictably, the upcoming six generation (6G) networks demand ultra-massive interconnectivity comprising densely congested sustainable small-to-tiny networks. The conventional radio access network (RAN) will be redesigned to provide the necessary intelligence in all areas to meet required network flexibility, full coverage, and massive access. In this respect, this paper focuses on intelligent massive RAN (mRAN) architecture and key technologies fulfilling the requirements. Particularly, we investigate potential artificial intelligence algorithms for network and resource management issues in 6G mRAN. Furthermore, we summarize the research issues in edge technologies and physical layer intelligence on 6G network architecture.

Abstract IL-27 is an IL-12 family cytokine with immune regulatory properties, capable of modulating inflammatory responses, including autoimmunity. While extensive studies investigated the major target cells of IL-27 mediating its functions, the source of IL-27 especially during tissue specific autoimmune inflammation has not formally been examined. IL-27p28 subunit, also known as IL-30, was initially discovered as an IL-27-specific subunit, and it has thus been deemed as a surrogate marker to denote IL-27 expression. However, IL-30 can be secreted independently of Ebi3, a subunit that forms bioactive IL-27 with IL-30. Moreover, IL-30 itself may act as a negative regulator antagonizing IL-27. In this study, we exploited various cell type specific IL-30-deficient mouse models and examined the source of IL-30 in a T cell mediated autoimmune neuroinflammation. We found that IL-30 expressed by infiltrating and CNS resident APC subsets, infiltrating myeloid cells and microglia, is central in limiting the inflammation. However, dendritic cell-derived IL-30 was dispensable for the disease development. Unexpectedly, in cell type specific IL-30 deficient mice that develop severe EAE, IL-30 expression in the remaining wild-type APC subsets is disproportionately increased, suggesting that increased endogenous IL-30 production may be involved in the severe pathogenesis. In support, systemic recombinant IL-30 administration exacerbates EAE severity. Our results demonstrate that dysregulated endogenous IL-30 expression may interfere with immune regulatory functions of IL-27, promoting encephalitogenic inflammation in vivo.

Abstract The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be presented. We developed four machine learning-based CVD classifiers (i.e., multi-layer perceptron, support vector machine, random forest, and light gradient boosting) based on the Korea National Health and Nutrition Examination Survey. We resampled and rebalanced KNHANES data using complex sampling weights such that the rebalanced dataset mimics a uniformly sampled dataset from overall population. For clear risk factor analysis, we removed multicollinearity and CVD-irrelevant variables using VIF-based filtering and the Boruta algorithm. We applied synthetic minority oversampling technique and random undersampling before ML training. We demonstrated that the proposed classifiers achieved excellent performance with AUCs over 0.853. Using Shapley value-based risk factor analysis, we identified that the most significant risk factors of CVD were age, sex, and the prevalence of hypertension. Additionally, we identified that age, hypertension, and BMI were positively correlated with CVD prevalence, while sex (female), alcohol consumption and, monthly income were negative. The results showed that the feature selection and the class balancing technique effectively improve the interpretability of models.

Abstract Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances.

Foxp3+ regulatory T (Treg) cells are a CD4 T cell subset with unique immune regulatory function that are indispensable in immunity and tolerance. Their indisputable importance has been investigated in numerous disease settings and experimental models. Despite the extensive efforts in determining the cellular and molecular mechanisms operating their functions, our understanding their biology especially in vivo remains limited. There is emerging evidence that Treg cells resident in the non-lymphoid tissues play a central role in regulating tissue homeostasis, inflammation, and repair. Furthermore, tissue-specific properties of those Treg cells that allow them to express tissue specific functions have been explored. In this review, we will discuss the potential mechanisms and key cellular/molecular factors responsible for the homeostasis and functions of tissue resident Treg cells under steady-state and inflammatory conditions.

The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.

As valuable as the Internet of Things (IoT) has been to explore and monitor the land surface of the earth, the Internet of Underwater Things (IoUT) has been crucial to the investigation of the underwater environment. Due to the unique characteristics of the IoUT, such as long propagation delays, high bit error rates, and limited bandwidth, acoustic sensors that use sound waves are preferred for deployment. Consequently, it is difficult to apply the medium access control (MAC) protocol designed for the IoT sensors that use radio frequencies (RFs) in the IoUT. Particularly for densely populated sensor networks, the challenging and dynamic underwater environment leads to the loss of control and data packets, resulting in energy waste and unreliable connections. In addition to a sensor being constrained by energy, replacing its battery is infeasible. Therefore, packet loss and energy conservation should be handled at the MAC layer in order to improve the throughput and longevity of the network. In this paper, we propose a REceiver-Initiated MAC based on Energy-efficient Duty cycling called REMEDY-MAC. Using autonomous underwater vehicles (AUV) for data collection, REMEDY allows a group of nodes to conserve energy by announcing the duty cycling schedule until an AUV arrives at a certain location. Once an AUV reaches a location, it receives data according to a pre-established schedule managed by the active sender node and moves to the next set of nodes after completion of its tasks. The simulation results indicate that the proposed REMEDY protocol significantly outperforms other MAC schemes with respect to network parameters such as energy efficiency.

In recent years, with the rapid development of information technology and the increasing maturity of communication technology, embedded computing technology, sensing technology and automatic control technology have made tremendous impact on our lives. The perfect integration of computing process and physical process Internet-of-Things (IOT) has attracted much attention from government, academia and industry.

Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km2 snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, QΔD) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the QΔD and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of QΔD and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and QΔD; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and QΔD; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible.

In vehicular networks, safety applications allow people to avoid hazardous situations based on the state of the vehicles in their proximity. Such proximity awareness is realized by allowing each vehicle to collect safety messages called beacons, which are periodically and locally broadcasted from its neighboring vehicles. Hence, the reliability of beacon transmissions is a crucial factor that makes safety applications effective in practice. Particularly, in respect of avoiding risky situations, lossy and scarce vehicular channel should be utilized primarily for reliable delivery of beacons from neighboring vehicles, which are more likely to cause dangerous situations such as collision. However, without consideration to such a requirement of safety applications, existing retransmission schemes treat every vehicles equally and are focusing on improving the retransmission performance in terms of loss recovery. In this paper, we therefore propose a new network coding-based repetition scheme called Context-Aware Network COded REpetition (CANCORE) for maximizing the effectiveness of safety applications. Using knowledge of contextual information (i.e., position, heading, and so on) of vehicles, CANCORE generates coded repetitions allowing more receivers to acquire beacons useful for avoiding impending dangerous situations. Our simulation study verified that CANCORE outperforms existing schemes in terms of its impact on the application-level performance (i.e., the accuracy of proximity awareness).

Named data networking (NDN) treats content/data as a “first class citizen” of the network by giving it a “name”. This content “name” is used to retrieve any information, unlike in device-centric networks (i.e., the current Internet), which depend on physical IP addresses. Meanwhile, the smart home concept has been gaining attention in academia and industries; various low-cost embedded devices are considered that can sense, process, store, and communicate data autonomously. In this paper, we study NDN in the context of smart-home communications, discuss the preliminary evaluations, and describe the future challenges of applying NDN in smart-home applications.

Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.

Multi-access edge computing (MEC) has become an essential technology for collecting, analyzing, and processing data generated by widely distributed user equipment (UE), wireless end-hosts, Internet of things (IoT) sensors, etc., providing real-time and high-quality networking services with ultralow end-to-end latency guaranteed between various user devices and edge cloud computing nodes. However, the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient mainly because of the operation and management constraints (e.g., limited space and capacity), particularly in the case of on-demand and dynamic service resource deployment. In this regard, we propose a selective MEC resource allocation scheme adopting a multitier architecture over a wide-area software-defined network (SDN) on the basis of our recent research work on virtual network slicing and resource orchestration. The proposed scheme provides an optimized MEC selection model considering end-to-end latency and efficient service resource utilization on the basis of the hierarchical MEC service architecture.

This study applied the remote sensing-based drought index, namely, the Energy-Based Water Deficit Index (EWDI), across Mongolia, Australia, and Korean Peninsula for the period between 2000 and 2010. The EWDI is estimated based on the hydrometeorological variables such as evapotranspiration, soil moisture, solar radiation, and vegetation activity which are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The estimated EWDI was compared with the Evaporative Stress Index (ESI), the Vegetation Condition Index (VCI), and the Standardized Precipitation Index (SPI). The correlation coefficients between the drought indices are as follows: 0.73–0.76 (EWDI vs ESI), 0.64–0.71 (EWDI vs VCI), 0.54–0.64 (EWDI vs SPI-3), 0.69–0.71 (ESI vs VCI), 0.55–0.62 (ESI vs SPI-3), and 0.53–0.57 (VCI vs SPI-3). The drought prediction accuracy of each index according to error matrix analysis is as follows: 83.33–94.17% (EWDI), 70.00–91.67% (ESI), 47.50–85.00% (VCI), and 61.67–88.33% (SPI-3). Based on the results, the EWDI and ESI were found to be more accurate in capturing moderate drought conditions than the SPI at different geographical regions.

For estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. The ANN model was regularized and trained using a set of 19,923 data points, observed daily in South Korea between 1960 and 2016. Leave-one-out cross validation was performed to validate the model. When the input data were known at the gauged locations, the correlation coefficient between the observed MDFSA and the estimated one by the ANN model was 0.90. When the input data were spatially interpolated at ungauged locations using the ordinary kriging (OK) method, the correlation coefficient was 0.40. The difference in correlation coefficients between the two methods implies that, while the ANN model itself has good performance, a significant portion of the uncertainty of the estimated MDFSA at ungauged locations comes from high spatial variability of the input variables that cannot be captured by the network of in situ gauges. However, these correlation coefficients were significantly greater than the correlation coefficient obtained by spatially interpolating the MDFSA values with the OK method (R = 0.20). These findings suggest that our ANN model significantly reduces the uncertainty of the estimated MDFSA caused by its high spatial variability.

Following publication of the original article [1], the authors reported that Figs. 3 and 6 are incorrect.

This study analyzed the spatial variability of the linear trend of the precipitation mean, variance, lag-1 autocorrelation coefficient, and probability of dryness (PD) based on the precipitation data between 1981 and 2015 observed at 65 rain gages located across Korean Peninsula. While the result of the Mann-Kendall test based on the yearly statistics showed no temporal trend at most of the gage locations, the same test based on the 20-yearly statistics showed that statistically significant temporal trend exists at 54% (mean), 60% (variance), 61% (autocorrelation), and 61% (PD) among the total 65 rain gages. In addition, this study produced the map of the linear trend of the precipitation statistics. The map showed a clear regional and seasonal tendency implying that the impact of the climate change varies significantly within Korea. The variogram analysis revealed that the approximate characteristic scale of linear trend of hourly and daily precipitation statistics ranges between 50 km and 200 km and between 100 km and 250 km, respectively. This characteristic scale is significantly smaller than the spatial scale of atmospheric circulation, which suggests that future water resources management plans of Korea should consider this mesoscale variability that otherwise can be missed if it is based only on the GCM simulation results.

Driver health and activity monitoring is one of the principal design issues for the safety provision in vehicular environments. Recently, the wireless sensor network technology is widely used to address the concerns in such applications. However, only few conventional protocols have dealt with reliable and prompt delivery of emergency packets considering the vehicular specifications. In this paper, we propose an emergency adaptive communication protocol, which treats the data packet in a discriminatory manner by investigating whether it is emergency or not. Hence, the proposed protocol defines an emergency factor for each data packet and exploits it for both route establishment and channel access procedures. In route establishment, the proposed protocol chooses a route with low delay and high reliability among the candidates by periodic calculation of emergency factor. Then, it dynamically adjusts back-off parameters before participating in the channel contention among the neighbors. In addition, an emergency aware queue management scheme and packet drop policy are proposed to improve the reliability of emergency data traffic during transmission. Our simulation results show that the proposed protocol provides a better performance compared with the existing protocol in terms of packet delivery ratio, end-to-end packet delay, and number of dropped packets.

Beacon or safety messages are broadcasted in vehicular ad hoc networks (VANETs) to disseminate network state or emergency incident information to other vehicles in the network. The freshness of information depends upon the frequent transmission of beacons. Similarly, to increase the awareness area or communicating with the distant nodes, beacons are disseminated with a high transmission power. However, increasing the beacon transmission power or rate has a negative effect on information communication efficiency because of the finite bandwidth of the wireless link. Therefore, different schemes have been proposed to individually control beacon's transmission power, transmission rate, or contention window at the MAC layer, or any combination of those, to achieve quality beacon communication in VANETs. The latter case is called hybrid adaptive beaconing schemes. In literature, there are many hybrid adaptive beaconing schemes that control multiple communication parameters to efficiently broadcast beacon messages in VANETs. In this paper, we explicitly survey and summarized various aspects of those schemes. The open and challenging issues are also highlighted in this paper.

Recently, the delay tolerant networks (DTN) have been utilized in various operational communication paradigms. This includes the communication scenarios that are subject to disruption and disconnection as well as the scenarios with high delay and frequent partitioning, that is, vehicular ad hoc networks (VANETs). Due to the several characteristics match, new research paradigm named as vehicular delay tolerant networks (VDTNs) is introduced. Through relays and store-carry-forward mechanisms, messages in VDTNs can be delivered to the destination without an end-to-end connection for delay-tolerant applications. However, the choice of routing algorithms in VDTNs is still under study. The main objective of routing protocols in VDTNs is to maximize the probability of delivery at the destination while minimizing the end-to-end delay. Until now, many routing protocols have been proposed to meet requirements of varying applications. In this paper, we, therefore, provide a detailed study of recently proposed routing schemes for VDTNs. We also perform comparative analysis on the basis of unique criterion such as forwarding metrics with their implementations. In addition, open challenges and future directions are provided to make room of interest for the research community.

Underwater acoustic sensor networks (UASNs) consist of remotely deployed sensor nodes under sea or other water environments. Due to the extreme limitations faced by radio signals under water, acoustic channels are utilized for communication in such networks. However, UASNs are challenged by the characteristics of underwater acoustic channels such as lower signal propagation speed and higher signal attenuation. On top of such a challenged physical medium, MAC schemes which are designed based on their terrestrial counter parts are required to add extra overheads to the communication channel wasting the limited network resources. MAC layer overheads such as bandwidth wastage for interframe spaces and contention for occupying physical medium put limitations to the maximum reachable throughput of UASNs. IEEE 802.11n has well defined various MAC and physical layer enhancements to overcome throughput barrier in wireless LANs which includes two frame aggregation schemes, namely, A-MPDU and A-MSDU. In this paper, we study the feasibility of applying those frame aggregations well defined in IEEE 802.11n for reducing MAC layer overheads in UASNs. Based on simulation studies, we evaluate that these frame aggregation schemes are applicable in UWSNs.

Although the IEEE 802.15.4a network provides accuracy localization for sensor nodes, it still suffers from congestion and bottleneck problems since the data traffic tends to concentrate on a certain intermediate node due to the shortest path first criteria when it utilizes a greedy routing method. Furthermore, the limited network bandwidth and node mobility features exacerbate this problem in wireless sensor networks with tiny sensor platforms. In this paper, we propose a new dynamic load aware geographical routing protocol, named DLAG, which periodically monitors the channel condition of each node and forwards a packet to the neighboring node with the least traffic load by defining new buffer threshold values for controlling the congestion. In addition, the proposed protocol also introduces traffic adaptive backoff and frame retransmission tuning techniques to provide prioritized channel access for congested nodes. In order to verify the performance of the proposed protocol, we conduct simulation verification experiments and the results show that the proposed protocol provides better performance than the legacy geographical routing schemes in terms of packet delivery ratio, end-to-end delay, network lifetime, and so forth.

Nowadays, wireless sensor networks (WSNs) are used in a variety of areas. However, it is difficult to efficiently manage a large number of sensor nodes and their sensing data owing to the limitations of WSNs. Particularly, in agricultural applications, a WSN installed in a specific region is used for multiple services (i.e., greenhouse environment monitoring and control). However, the network resources (i.e., channel, battery, etc.) are currently being utilized for redundant operations requested from multiple service users owing to the lack of an efficient system for managing large WSN data. In this paper, we propose an agriculture sensor-cloud infrastructure (ASCI) to effectively provide various agricultural services using WSNs. In addition, we propose hierarchical source routing (HSR), aggregation gradient routing (AGR), and a priority-based data transmission technique in order to allow packets to be delivered to the destination fast and reliably in large-scale WSNs.

In underwater wireless sensor networks (UWSNs), flooding-based routing protocols are preferred due to their capability of reducing the routing overhead in terms of no need of path setup and maintenance. In addition to routing, a transport protocol should be developed to recover lost DATA segments in loss-prone UWSNs. In particular, due to long propagation delay in UWSNS, a fast end-to-end recovery technique needs to be developed. Fortunately, the Fast Retransmit technique well-defined in TCP can be exploited for fast retransmissions of lost DATA segments. However, if it operates over the flooding-based routing protocols, each source node will receive multiple copies of ACK segments which are transmitted along different paths. Therefore, the source node cannot distinguish between these multiple copies of ACK segments and real duplicated ACK segments (defined as an ACK indiscretion problem in this paper), which leads to unnecessary retransmissions. In this paper, we therefore propose two Fast Retransmit techniques to address the ACK indiscretion problem. In our first proposed technique, the sink records a new count number into a header, informing how many duplicated ACK segments have been transmitted from the sink. Since this requires an additional field in the header for the count number, the second technique allows a source node to estimate the number of ACK copies which are expected to be received when the sink transmits an ACK segment, without any dependency on the additional field. From both of our proposed techniques, the source nodes become aware of the accurate number of duplicated ACK segments transmitted from the sink and can perform the Fast Retransmit correctly.

A novel approach for a Poisson cluster stochastic rainfall generator was validated in its ability to reproduce important rainfall and watershed response characteristics at 104 locations in the United States. The suggested novel approach, The Hybrid Model (THM), as compared to the traditional Poisson cluster rainfall modeling approaches, has an additional capability to account for the interannual variability of rainfall statistics. THM and a traditional approach of Poisson cluster rainfall model (modified Bartlett-Lewis rectangular pulse model) were compared in their ability to reproduce the characteristics of extreme rainfall and watershed response variables such as runoff and peak flow. The results of the comparison indicate that THM generally outperforms the traditional approach in reproducing the distributions of peak rainfall, peak flow, and runoff volume. In addition, THM significantly outperformed the traditional approach in reproducing extreme rainfall by 2.3% to 66% and extreme flow values by 32% to 71%.

IEEE 802.11p/1609-based vehicular networks utilize a multichannel architecture to support vehicle-to-vehicle and vehicle-to-infrastructure communications. In the multi-channel architecture, the available channels in the 5 GHz spectrum are divided into one control channel (CCH) and multiple service channels (SCHs). Multiple SCHs are defined for nonsafety data transfer, while the CCH is used to broadcast safety messages called beacons and control messages (i.e., service advertisement messages). According to the channel coordination scheme, a radio interface alternately switches between the CCH and a specific SCH. The intervals during which a radio interface stays tuned to the CCH and SCH are called CCH and SCH intervals, respectively. Both intervals are set to a fixed value (i.e., 50 ms) in the standard. However, since the fixed-length intervals cannot be effective for dynamically changing traffic load, some dynamic interval division protocols have been recently proposed to support the dynamic adjustment of the CCH/SCH intervals for improving channel utilization. In this paper, we therefore provide a survey of dynamic interval division protocols for VANETs, discuss the advantages and disadvantages of them, and define some open issues and possible directions of future research.

A wireless sensor network (WSN) has become an important technology and has been deployed in many emerging applications, including home automation, health care, and precision agriculture. To deploy more service applications, the WSN must solve the internetworking problem (difference in speed, protocol stack, beacon collision etc.). In this study, we propose a redundant-path scheme based on a multichannel WSN for an efficient interworking among the heterogeneous networks. We evaluate the performance of our proposed scheme through a real-world simulation, and the results show an improvement in data throughput, packet delay, and beacon loss ratio.

In wireless sensor networks, clustering expedites many desirable functions such as load balancing, energy savings, and distributed key management. For secure clustering, it is very important to find compromised nodes and remove them during the initial cluster formation process. If some nodes are compromised and survive the censorship process, they can make some nodes have a different cluster view and can split a cluster into multiple clusters to deteriorate cluster quality as a whole. To resolve these problems, we propose a robust scheme against such attacks in this paper. First, our scheme generates large-sized clusters where any two nodes are at most two hops away from each other to raise the quality of clusters. Second, our scheme employs the verification of two-hop distant nodes to preserve the quality of the large-sized clusters and refrains from splitting the clusters. Last, our scheme prefers broadcast transmissions to save the energy of nodes. Security analysis proves that our scheme can identify compromised nodes and preserves the cluster membership agreement against the compromised nodes. In addition, simulation results prove that our scheme generates fewer clusters and is more secure and energy efficient than the scheme producing only small-sized clusters.

Recently, underwater wireless sensor networks (UWSNs) have attracted much research attention from both academia and industry, in order to explore the vast underwater environment. UWSNs have peculiar characteristics; that is, they have large propagation delay, high error rate, low bandwidth, and limited energy. Therefore, designing network/routing protocols for UWSNs is very challenging. Also, in UWSNs, improving the energy efficiency is one of the most important issues since the replacement of the batteries of underwater sensor nodes is very expensive due to the unpleasant underwater environment. In this paper, we therefore propose an energy efficient routing protocol, named (energy-efficient depth-based routing protocol) EEDBR for UWSNs. EEDBR utilizes the depth of sensor nodes for forwarding data packets. Furthermore, the residual energy of sensor nodes is also taken into account in order to improve the network lifetime. Based on the comprehensive simulation using NS2, we observe that EEDBR contributes to the performance improvements in terms of the network lifetime, energy consumption, and end-to-end delay. A previous version of this paper was accepted in AST-2011 conference.