Robust brain-tumor segmentation in MRI must withstand domain shifts from heterogeneous scanners and protocols. Unsupervised domain adaptation (UDA) can exploit plentiful unlabeled data, yet many approaches erode boundaries, suppress tumor cues, or amplify errors from noisy pseudo-labels. Generalpurpose models like SAM also struggle on MRI due to the domain gap and a lack of morphology-aware consistency. We propose SAM-RefiSeR, a two-phase UDA framework that integrates SAM for reliable, annotation-efficient segmentation. Phase I reduces source-target discrepancy via Fourier-based frequency adaptation and adversarial feature alignment, aligning appearance and representations while preserving anatomy. Phase II adopts a student-teacher scheme in which SAM first refines pseudo-labels, then gates them with confidence- and morphology-aware criteria to suppress unreliable masks and curb error propagation. Across diverse cross-modality settings, SAM-RefiSeR consistently surpasses strong UDA baselines, particularly under severe shifts. By improving boundary fidelity and robustness without additional labels, SAM-RefiSeR brings brain-tumor segmentation closer to practical, generalizable clinical deployment.

Glaucoma is one of the leading causes of blindness worldwide, where early and precise diagnosis is critical for effective treatment. We explore the potential of deep learning to generate a Visual Field Grayscale Map (GM) directly from a Conventional Fundus Image (CFI) at the same time point, enabling an intuitive representation of visual field defects for the diagnosis of glaucoma. The Humphrey Visual Field (HVF) test is a standard method for assessing visual field defects, but its dependency on active participation and prolonged testing time limits its reliability, particularly in elderly patients. To address these challenges, we propose CFI2GM-GAN, a novel deep learning framework that directly generates GM from CFI, offering a faster and cost-effective alternative while complementing the HVF test. Our method incorporates dataset partitioning based on defect severity to mitigate class imbalance, attention-guided fusion techniques for improved prediction reliability, and a refined loss function customized to fundus image characteristics.

Conditional Handover (CHO) by the 3rd Generation Partnership Project (3GPP) enables efficient user mobility between Base Stations (BSs) by preselecting and preparing Target BSs (T-BSs). However, CHO relies on signal strength for T-BS selection, leading to resource blocking on multiple T-BSs due to signal fluctuations. Existing state-of-the-art methods use deep learning to narrow the list of T-BSs but still lack an effective method for resource reservation timing. This paper presents in-time CHO (iCHO) which exploits historical mobility data to estimate user dwell time at the current BS to reduce resource reservation duration. The proposed iCHO employs a Multivariate Multi-output Single-step Prediction (MMSP) model that leverages a multi-task learning approach to simultaneously predict the minimal list of required T-BSs together with the user dwell time. The model demonstrates remarkable performance across two mobility datasets of different scales, achieving T-BS prediction accuracies of 98% and 95%. It also ensures a 100% handover success rate with a minimum of three and four predicted T-BSs for both datasets, respectively, significantly limiting the list of T-BSs. Moreover, the MMSP model achieves a Mean Absolute Error (MAE) of 19 s and 45 s when predicting the user’s dwell time at the current BS. By utilizing these predictions, iCHO reserves resources at the minimum number of T-BSs immediately before handover. Thus, iCHO can save up to 99% of resources from blockage as compared to the CHO, enabling operators to increase revenue by serving up to eighteen more users with the saved resources.

An automated analysis of respiratory sounds using Deep Learning (DL) plays a pivotal role in the early detection of lung diseases. However, current DL methods often examine the spatial and temporal characteristics of respiratory sounds in isolation, which inherently limit their potential. This study proposes a novel DL framework that captures spatial features through convolution operations and exploits the spatiotemporal correlations of these features using temporal convolution networks. The proposed framework incorporates Multi-Level Temporal Convolutional Networks (ML-TCN) to considerably enhance the model accuracy in detecting anomaly breathing cycles and respiratory recordings from lung sound audio. Moreover, a transfer learning technique is also employed to extract semantic features efficiently from limited and imbalanced data in this domain. Thorough experiments on the well-known ICBHI 2017 challenge dataset show that the proposed framework outperforms state-of-the-art methods in both binary and multi-class classification tasks for respiratory anomaly and disease detection. In particular, improvements of up to 2.29% and 2.27% in terms of the Score metric, average sensitivity and specificity, are demonstrated in binary and multi-class anomaly breathing cycle detection tasks, respectively. In respiratory recording classification tasks, the classification accuracy is improved by 2.69% for healthy-unhealthy binary classification and 1.47% for healthy, chronic, and non-chronic diagnosis. These results highlight the marked advantage of the ML-TCN over existing techniques, showcasing its potential to drive future innovations in respiratory healthcare technology.

The mobile network traffic patterns in urban areas significantly diverge depending on commercial and residential establishments. These regional traffic patterns provide crucial clues for predicting traffic patterns precisely. Previous studies have employed a combination of time-series and convolutional Deep Learning (DL) models to effectively capture the correlation of the regional features and traffic patterns. Despite promising results, these approaches are limited in identifying pattern similarities among sparsely located regions and can be further improved. To this end, this study proposes a GEospatial clustering and residual COnvolutional temporal long Short-term memory (GECOS) framework consisting of clustering and DL components. The proposed Urbanflow Peak Clustering (UPC) component exploits the peak traffic times of daily mobile data to obtain the groups of cells with similar traffic patterns apart from their geographical diversity. The UPC improves the scalability of existing algorithms and enables DL components to improve their accuracy by recognizing unique regional patterns and localizing the training targets. The proposed Residual Convolutional TCN-LSTM (RCTL) serves as the DL component of GECOS that improves TCN-LSTM structure through layer-wise feature transfer and enhances long-term dependency learnability. The RCTL ensures more accurate capturing of extensive spatiotemporal features through structural enhancements. The experiments conducted on real-world mobile traffic data showcase 43% improvement by GECOS compared to state-of-the-art models, enabling precise traffic engineering policies by operators.

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.

The advent and anticipated evolution of Beyond Fifth Generation (B5G) networks raise critical issues for the static Time Division Duplex (TDD) radio resource allocation technique. In Static TDD, the fixed allocation of uplink and downlink resources leads to poor resource utilization, with uplink channels often congested and downlink channels underutilized. This study addresses static TDD limitations by proposing a novel TDD configuration called Traffic-Aware Dynamic TDD (TA-TDD), aiming to satisfy the high-speed and low-latency communication requirements of various applications. Specifically, the proposed TA-TDD utilizes Convolutional Long Short-Term Memory (ConvLSTM) model to predict traffic before allocation of uplink and downlink resource. This method effectively manages uplink-centric traffic in wireless networks, to improve both network quality and user experience. Compared to static TDD, the proposed TA-TDD notably improves network throughput by as much as 20% in scenarios with high uplink demand. The findings demonstrate that dynamic TDD configurations significantly enhance network throughput compared to static setups, which offers an effective solution for network management.

The recent surge in mobile traffic has increasingly underscored the importance of Edge AI. The Edge Server (ESs) in Edge AI facilitate precise traffic prediction by collecting regional data and analyzing the characteristics and traffic patterns of adjacent areas. However, existing Edge AI systems for mobile traffic prediction are limited by their reliance on physical proximity for regional selection, failing to effectively leverage the unique infrastructure and lifestyle patterns of each area. This study proposes a novel Edge AI mobile traffic prediction architecture that overcomes the performance limitations of traditional methods by integrating multi Temporal Convolutional Networks-Long Short Term Memory (TCN-LSTM) with clustering techniques that reflect regional characteristics. The proposed approach is unconstrained by distances between regions, hence maximally utilizing unique features of each area. Furthermore, by incorporating Federated Learning (FL), this study significantly reduces the computational load, optimizing the model for real-world applications. The effectiveness of this model is validated across various Edge AI scenarios of different sizes, demonstrating a performance improvement of approximately 30% in Mean Absolute Percentage Error (MAPE) compared to conventional Edge AI system.

Registration areas (RAs) play a pivotal role in the localization of UEs in B5G/6G mobile networks for instant service delivery, as they define a region where the network is certain of UE presence in active and idle modes. A UE must update its registration with the network when it changes its RA, hence, it is desirable to increase RA sizes to minimize registration updates but this elevates the paging overhead and vice versa. Conventionally, RAs are manually defined at the network initiation and they largely remain static afterward. This preliminary study proposes a tailored dynamic registration approach, where a dynamic RA is tailored for a UE according to its movement pattern and rate. This is achieved through a Lightweight Recurrent deep learning Model (LRM) that approximates the region of UE presence for the next defined period. The proposed input sequence aggregation and output sequence compression mechanisms in LRM significantly reduce the computational footprint. The preliminary evaluation with open-source dataset confirms that tailored dynamic registration achieves tradeoff between paging and registration and reduces their signaling overheads by an average 54% and 65%, respectively, compared to conventional static RAs. Further, an average 51% reduction in learning time by LRM showcases its robustness and practical viability.

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

Data aggregation is an important approach in IoT sensor networks since it reduces data transfer while also preserving energy and bandwidth. This research investigates the challenge of time-efficient data aggregation in wireless sensor networks, which is critical in military, civilian, and industrial applications. Effective data aggregation algorithm design and optimization are required for quick and interference-free data collection. Machine learning has received attention for outperforming classical heuristic techniques. The research presents the first Graph Neural Network (GNN) model for data aggregation in IoT sensor networks, which incorporates Graph Attention Networks (GATs) and fully connected layers. The GNN-based model learns network topology and node attributes, creating node embeddings and correcting sensor node transmitting time slots. With a centralized training procedure and adapted execution for network size change, the proposed approach achieves satisfactory performance compared to the heuristic algorithm.

Fast data aggregation is crucial for facilitating critical Internet of Things services as it enables the collection of sensory data within strict volume and time constraints. Over the past decades, the data aggregation scheduling problem for minimum latency has garnered significant research attention. Existing approaches to this problem typically schedule all data transmissions based on an aggregation tree, which is constructed without secondary interference. However, such interference can introduce delays when scheduling a transmission from a node to its parent in the tree. To this end, this study proposes an approach called Active Neighbor EXploitation (ANEX) that enables sensor nodes to switch their parents by identifying active neighbors for potential connectivity, irrespective of the receivers established in the tree. Additionally, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors, thereby allowing for more concurrent transmissions. ANEX is evaluated through theoretical analysis and extensive simulations under various scenarios. The results demonstrate that ANEX achieves up to 86% faster aggregation compared to the state-of-the-art approach while maintaining an equivalent time complexity.

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme.

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

The Smart City contexts and the adoption of Industry 4.0 are being upgraded with the latest technologies throughout all systems. New data are continuously collected in huge amounts; therefore, an efficient data aggregation scheduling scheme is highly demanded. This paper addresses the minimum time aggregation scheduling problem in duty-cycled sensor networks. Existing solutions schedule the sensory data through predefined routing structures, which limit the utilization of diverse active time slots of sensors. We propose a period-driven pipeline scheduling approach, namely PDA, that simultaneously grows the aggregation tree and assigns a transmission schedule for each node being added to the tree. Particularly, this process is performed in a top-down manner. In each iteration, corresponding to a time slot, PDA uses a multi-level ranking strategy to schedule several$\langle sender, receiver \rangle$pairs, so that in a working period ahead, the possibility to pipeline as many transmissions as possible is high. Intensive simulation results show that the proposed scheme notably works better than the best known recent algorithms by having up to 35 percent shorter aggregation time, as well as having a significantly improved network throughput and time utilization.

Generative adversarial network (GAN)-based methods recover perceptually pleasant details in super resolution (SR), but they pertain to structural distortions. Recent study alleviates such structural distortions by attaching a gradient branch to the generator. However, this method compromises the perceptual details. In this paper, we propose a sparse gradient-guided attention generative adversarial network (SGAGAN), which incorporates a modified residual-in-residual sparse block (MRRSB) in the gradient branch and gradient-guided self-attention (GSA) to suppress structural distortions. Compared to the most frequently used block in GAN-based SR methods, i.e., residual-in-residual dense block (RRDB), MRRSB reduces computational cost and avoids gradient redundancy. In addition, GSA emphasizes the highly correlated features in the generator by guiding sparse gradient. It captures the semantic information by connecting the global interdependencies of the sparse gradient features in the gradient branch and the features in the SR branch. Experimental results show that SGAGAN relieves the structural distortions and generates more realistic images compared to state-of-the-art SR methods. Qualitative and quantitative evaluations in the ablation study show that combining GSA and MRRSB together has a better perceptual quality than combining self-attention alone.

Anomaly detection is essential to guarantee the correctness of sensory data collected from Internet of Things. The latest detection approaches only operate under one of the two general forms of short-term or long-term anomaly. In this article, we propose monotone split and conquer (MSC) scheme to tackle both anomaly forms. The proposed scheme exploits the spatial–temporal correlation between neighboring sensors to detect abnormal sensory data. MSC splits the collected data into monotonic subtrends in the training phase to establish the trend-based normal profiles for the online detection phase. To eradicate the overfitting phenomenon, we further develop a general formulation to estimate the square prediction error (SPE) control limit. Both monotone split and general formulation contribute to the advancement of MSC in terms of accuracy (ACC) and false positive rate (FPR) in the online detection phase. To evaluate the performance of MSC, we design a general anomaly model to generate artificial short-term and long-term anomalies. Numerical experiments with the Intel Berkeley Research Lab (IBRL) data set demonstrate that MSC obtains about 8% higher ACC and 5% lower FPR on average when compared to existing schemes. Remarkably, MSC requires only a few observations for anomaly detection as it is applicable to real-time systems.

Internet-of-Things (IoT) systems and their applications rely on sensory data for operating context-aware functions. To ensure reliability, these data must arrive at the base station in a timely manner. Because IoT sensors are battery powered and expected to last for years, energy consumption has always been a dominant concern. The duty cycling mechanism efficiently extends the lifespan of sensor nodes; however, it increases data aggregation time. This article studies the minimum time aggregation scheduling problem in duty-cycled sensor networks. The existing solutions route the sensory data through a predefined structure, but such structures are constructed without full awareness of collisions in the wireless medium. Therefore, in this study, the collision-resistant dynamic (CORD) scheduling approach has been proposed with an aim to provide fresh data for emerging IoT applications. The proposed approach is applicable to any initial routing structure and it dynamically changes the receiver of a transmission whenever this change can reduce aggregation time. The receiver-changing decision is made on the fly, resisting all collisions between the determined transmissions. We evaluate CORD in various simulation scenarios, and the results demonstrate that the proposed approach yields a notable improvement of up to 62% from the viewpoint of aggregation time while having comparable time complexity compared to state-of-the-art methods.

Software-defined networking provides a programmable and flexible way to manage the network by separating and centralizing the control plane. The data plane entities like software-defined switches and routers use flow entries in flow tables for forwarding the packets. However, the limited switch memory restricts the number of flow entries in the flow tables. This leads to flow table overflow and flow entry reinstallation problems, which severely degrade the network performance. This requires a comprehensive policy for timely eviction of inactive flow entries to avoid overflows and optimally maintain flow tables usage. To this end, many studies have been proposed, but none of them have suggested detailed eviction strategy for UDP flows. This paper proposes a UDP flow eviction strategy which periodically updates the statistical information of UDP flows through reinforcement learning and utilizes it to evict inactive UDP flows. This eviction strategy is combined with the existing TCP flow eviction method to form an eviction system that takes into account the protocol-specific characteristics of the flow. Through three traffic-based experiments, we found that the proposed system reduces the number of overflow occurrences by 27% and flow entries reinstallation by 28%, compared to the random and FIFO policies, resulting in 15% reduction in control signaling overhead.

Current reactive mobility management in cellular networks becomes a bottleneck for ultra-low latency 5G services and severely degrades the QoS. To satisfy the ultra-low latency requirement of 5G services, proactive mobility management is essential where next PoA of the user is predicted with minimal error. Recent studies have used different deep learning algorithms for this purpose, but their results are unacceptable in real networks due to low accuracy. This paper exploits the distributional learning capability of Generative Adversarial Network (GAN) to propose MoGAN for the prediction of user’s next PoA. The generator in MoGAN uses Gated Recurrent Unit to learn the distribution of time-series data and generates the next PoA. Meanwhile, the discriminator evaluates the generated output against the real data to determine its correctness. The model is trained in adversary mode by using the output from the discriminator. The dataset utilized in training and evaluation is collected from one of the university campuses, and the results show 96.33% of prediction accuracy, which is 5% higher than the previous study. Furthermore, MoGAN is more robust under limited data conditions, as it achieves above 90% accuracy with only 50% of the dataset.

Minimum latency scheduling has arisen as one of the most crucial problems for broadcasting in duty‐cycled Wireless Sensor Networks (WSNs). Typical solutions for the broadcast scheduling iteratively search for nodes able to transmit a message simultaneously. Other nodes are prevented from transmissions to ensure that there is no collision in a network. Such collision‐preventions result in extra delays for a broadcast and may increase overall latency if the delays occur along critical paths of the network. To facilitate the broadcast latency minimization, we propose a novel approach, critical‐path aware scheduling (CAS), which schedules transmissions with a preference of nodes in critical paths of a duty‐cycled WSN. This paper presents two schemes employing CAS which produce collision‐free and collision‐tolerant broadcast schedules, respectively. The collision‐free CAS scheme guarantees an approximation ratio of (Δ − 1)T in terms of latency, where Δ denotes the maximum node degree in a network. By allowing collision at noncritical nodes, the collision‐tolerant CAS scheme reduces up to 10.2 percent broadcast latency compared with the collision‐free ones while requiring additional transmissions for the noncritical nodes experiencing collisions. Simulation results show that broadcast latencies of the two proposed schemes are significantly shorter than those of the existing methods.

An essential requirement in operating a carriergrade network (CGN) is ensuring the high availability and reliability. Software-defined networking (SDN) is expected to address such requirement while improving the network management. One challenging issue faced in the process of enhancing the reliability of SDN-enabled CGN is how to achieve rapid recovery with minimal effort. There are two well-known approaches to determine the failover scope: end-to-end (global) detouring and local detouring. Particularly, the local detouring approach provides an efficient means to achieve faster recovery, as it locally detours the disrupted flows around the failed network components using a preconfigured alternative path. However, it requires thousands of flow entries per switch to be configured. To address the technical challenges, we propose a fault-tolerant forwarding table design (FFTD), which groups the flows using group entries and aggregates the flows using a tagging mechanism for scalable and rapid recovery from the dual-failures of switches or links without overburdening the controller and the flow table's memory. Our extensive emulation results reveal that the proposed FFTD satisfies the CGN's 50 ms recovery requirement. Additionally, it reduces the alternate path flow storage requirement by up to 99%.

A method is proposed for fully restoring local image structures of an unknown continuous-tone patch from an input halftoned patch with homogenously distributed dot patterns, based on a locally learned dictionary pair via feature clustering. First, many training sets consisting of paired halftone and continuous-tone patches are collected, and then histogram-of- oriented-gradient (HOG) feature vectors that describe the edge orientations are calculated from every continuous-tone patch, to group the training sets. Next, a dictionary learning algorithm is separately conducted on the categorized training sets, to obtain the halftone and continuous-tone dictionary pairs, optimized to edge-oriented patch representation. Finally, an adaptively smoothing filter is applied to the input halftone patch, to predict the HOG feature vector of an unknown continuous-tone patch, and to select one of the previously learned dictionary pairs, based on the Euclidean distance between the HOG mean feature vectors of the grouped training sets and the predicted HOG vector. In addition to using the local dictionary pairs, a patch fusion technique is used to reduce some artifacts, such as color noise and overemphasized edges on smooth regions. Experimental results show that the use of the paired dictionary selected by the local edge orientation and patch fusion technique not only reduced the artifacts in smooth regions, but also provided well expressed fine details and outlines, especially in the areas of textures, lines, and regular patterns.

Data aggregation is used to reduce the average number of transmissions and to save energy by aggregating multiple packets into a single packet in Wireless Sensor Networks (WSNs). Data aggregation scheduling problems have been extensively studied to make sure collision-free packet delivery to a sink node, with the goal of minimizing the aggregation time. Almost all existing data aggregation schemes have a continuous energy consumption problem of RF transceiver because they assume that each node is always in an active state. Recently, the data aggregation scheduling in duty-cycled WSNs is considered to reduce energy consumption. In this paper, we propose a delay-aware data aggregation scheduling scheme to minimize latency in duty-cycled WSNs. In a data aggregation tree construction phase, our proposed scheme constructs Connected Dominating Set (CDS) tree on the shortest path in terms of delay according to an active time slot of each node. The CDS tree is used as virtual backbone for efficient data aggregation. In a scheduling phase, the proposed scheme ensures collision-free data aggregation to the sink with reduced latency by considering data receiving time in each node. The simulation results show that comparing to related work, our proposed scheme can reduce the average time for data aggregation to the sink by 54% and 50% with various node densities and duty cycle respectively.

In a reactive handover, a Mobile Node (MN) handover procedure is not prepared before the event. Since such handovers are performed for roaming MNs in Proxy Mobile IPv6 (PMIPv6), there are limitations in reducing MN handover latency and preventing packet loss. If a handover is predictable and a handover can be prepared beforehand, the handover latency and packet loss can be minimized. To prevent packet loss and reduce handover latency during an MN handover, proactive handover schemes have been proposed as well as buffering schemes. However, these schemes do not determine the exact starting point of the MN handover. In this work, a scheme is proposed to a support proactive MN handover to prevent packet loss, and to minimize handover latency and buffering cost, based on the MN's location information in PMIPv6. Our proposed scheme decides the exact moment of the MN handover and the next network to which the MN roams based on the MN's location information. It is compared to existing schemes through mathematical analysis and simulation. The results of performance evaluation show that our proposed scheme reduces handover latency, signaling cost, and buffering cost effectively compared to the existing schemes.

This study examines the users' experiences with three-dimensional (3D) virtual environments to investigate the areas of development as a learning application. For the investigation, the modified technology acceptance model (TAM) is used with constructs from expectation-confirmation theory (ECT). Users' responses to questions about cognitive perceptions and continuous use were collected and analysed with factors that were modified from TAM and ECT. Whilst the findings confirm the significant roles by users' cognitive perceptions, the findings also shed light on the possibility of 3D application serving as an enabler of learning tools. In the extended model, the moderating effects of confirmation/satisfaction and demographics of the relationships amongst the variables were found to be significant.

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Tables in this paper, originally published in Behaviour & Information Technology, Volume 32, Number 2 (2013) pp. 156–172 are incorrect.Tables should read:AgeNumberPercentage (%)Under 204814.621–301...

Smart TV (STV), a new digital television service, has been rapidly developing, particularly in Korea. With the conceptual model of interactivity, this study empirically investigates the effects of perceived interactivity on the motivations and attitudes towards STV in Korea. The model is created to validate the relationship of perceived interactivity to performance, attitude and intention. Further, the model examines the mediating roles of perceived interactivity in the effect of performance on attitude towards STV. Empirical evidence supports the mediating role of perceived interactivity. Implications of the findings are discussed in terms of building a theory of interactivity and providing practical insights into developing a user-centred STV interface.

Route Optimization (RO) is a function to minimize packet transmission delay through the optimal path between routers communicating with each other. Mobile IPv6 (MIPv6) also supports the RO to solve the triangle routing problem. Basic PMIPv6 does not support the RO. Thus, many schemes have been proposed to support RO in PMIPv6. However, these schemes do not consider the out-of-sequence problem, in which packets arrive out of order, which may happen between the existing path and the newly established RO path. This paper proposes a scheme to solve the out-of-sequence problem more precisely and with low cost. Our proposed scheme solves the problem by using the packet sequence number and the time when the problem occurs. In this paper, we compare PMIPv6 supported by the RO and the Out-of-sequence Time Period (OTP) scheme with our proposed scheme via simulation. Evaluation of the performance reveals PMIPv6 supported by the RO had 66 of out-of-sequence packets, and the OTP scheme has 30. However, our proposed scheme does not incur out-of sequence packets. Our proposed scheme guarantees reliable packet transmission by preventing the problem.

Smartphone users in the U.S. and Korea were cross-surveyed to determine country-specific differences in product value perceptions. Usability factors and aesthetic values were combined using the theory of reasoned action (TRA). The strengths of the model’s relationships are discussed. The models were analyzed cross-nationally to explore differences in the compositions of technology adoption motives in the two countries. Although the results illustrate the importance of both usability and aesthetic values, the two countries show different value preferences as well as intention and adoption patterns. The results of this study suggest practical implications for employing cross-cultural strategies in the global marketing of smartphones as well as theoretical implications for cross-country studies, which are recommended accordingly.

ABSTRACT Secure distributed spectrum sensing (DSS) schemes in cognitive radio networks (CRNs) have been proposed to reliably detect the signals of the primary users (PUs) even when compromised nodes generate forged sensing reports. However, they have limited sensing accuracy for PU signals because of the absence of exact signal patterns of PUs. This is caused by Federal Communications Commission restriction on (no modification of) PUs, and thus, the CRNs cannot communicate with the PUs to obtain such patterns. We propose a verification framework utilizing primary user emulation signals that can be applied to existing DSS schemes to address this challenge. This will reinforce the robustness against forged sensing values. We then develop a concrete verification scheme based on this framework and an existing secure DSS scheme. We evaluate our approach via in‐depth simulation and analysis compared with the existing scheme. Results show that our approach improves sensing accuracy and fusion speed in the cases of attack. Copyright © 2011 John Wiley & Sons, Ltd.

Data Aggregation is a fundamental activity in wireless sensor networks. Recent studies in this area focus on giving collision-free scheduling and finding the upper bound of delay time in aggregation. In this paper, we propose a new scheduling strategy, based on Neighboring Dominators, to minimize the time latency in data aggregation. With the new scheduling strategy, we mathematically prove that the upper bound of delay time in Data Aggregation is at most 12R+Δ-11. Here R is the network radius and Δ is the maximum node degree in communication graph of network. Theoretical analysis proves that our strategy is significantly better than the previously well known results with the upper bounds at 16R+Δ-14 or 24D+6Δ +16 time slots, where D is network diameter (D could be as large as 2R).

Mobile Access Gateways and Local Mobility Anchors exchange mobility related signaling messages on behalf of Mobile Nodes (MNs) to support mobility in Proxy Mobile IPv6 (PMIPv6) networks. Thus, MNs maintain their connections whilst roaming network domains without the need of a mobility protocol stack. However, with the large number of MNs in a PMIPv6 domain, the loads on the network entities grow. In particular, LMA has higher probability of reaching a severe load level, since LMA handles every packet that all the MNs in the domain send and receive. Thus, this paper proposes a load balancing scheme for LMAs in PMIPv6 networks where more than one LMA exist in the domain. The proposed scheme distributes loads of LMAs evenly, therefore enhances the overall efficiency of the network. Simulation shows the proposed scheme effectively balances the loads of LMA and reduces total packet loss rate by 3%.

Data forwarding schemes that use a single ACK-based retransmission mechanism are widely used to increase the packet delivery ratio in wireless sensor networks. However, these schemes may waste a considerable amount of energy for data transmissions, especially when the link quality is low for ACK transmissions. In this paper, we propose a novel multi-ACK-based data forwarding scheme to minimize the energy consumption for unnecessary data retransmissions. The next hop forwarder selection significantly affects the energy efficiency of communications. Hence, we also develop a new next hop forwarder selection metric, called effective energy consumption (EEC), which makes the proposed data forwarding scheme suitable for geographic routing protocols. Mathematical analysis and simulation results demonstrate that the proposed multi-ACK data forwarding scheme can save much energy and reduce the total amount of traffic load in the network. More specifically, the combination use of multi-ACK data forwarding and EEC metric increases the energy efficiency by 47.8% when node density is low, and 75.0% when node density is high, compared to the geographic forwarding protocols which use a well-known metric, PRR × Distance.

In wireless sensor networks, density control is a promising approach that has a considerable impact on extending the network lifetime. To optimize the energy utilization, the sensors in active mode adjust their sensing ranges to fulfill two conflicting objectives, minimizing the overlapping sensing area of the sensors and at the same time maintaining a high degree of coverage. In this paper, we consider the problem of energy-efficient area coverage by adopting various node scheduling models for sensor networks with adjustable sensing ranges. We construct a universally elemental pattern that can adapt its topology form to generate a set of new energy-efficient models, since recent exploration of node scheduling models has not been conducted in a systematic manner. Mathematical results and extensive simulation highlight important remarks for practical applications and show that the new models outperform existing ones in terms of energy efficiency.

A mobile agent system is regarded as an attractive technology when developing distributed applications. However, mobility makes it more difficult to trace agents. It is also more complex for agents to communicate with each other in a reliable manner. Therefore, a reliable communication protocol is necessary to control and monitor mobile agents and deliver messages between them. In this paper, a new reliable communication protocol (RCP) is proposed for a multiregion mobile agent computing environment. RCP is implemented on the ODDUGI mobile agent system. Analysis and evaluation show that RCP fulfills the following design goals: reliability, asynchrony, timeliness, location dependency, scalability, and communication cost.

This paper presents a novel probabilistic approach to joint attention. Joint attention is a communicative activity that allows communicators to share perceptual experience by attending on the same visual object. This communicative activity is conceptualized as a conditional probability over jointly given test and cue stimuli. To formalize the joint attention with mathematical terms, our approach starts from a simple decision task in which a response of subjects is determined by a test stimulus only. Our approach extends to an attentional cueing task in which subjects make a decision on a test that are jointly given with a cue stimuli. The joint relationship between test and cue stimuli yields attentional cueing effects -- faster and more accurate response if the two stimuli are consistent and slower and less accurate response if not. With our model, a series of simulations were carried out to show interesting properties of the model that can not be captured using a test stimulus alone. The model successfully locates a visual object guided by a cue stimulus such as color and pointing gesture. These results indicate that joint attention can be considered as a cooperative decision process on a visual object among many objects with a referential cue driven from a communicator.

Existing routing schemes assume an ideal environment where any form of transmission always succeeds. However, in realistic wireless sensor networks environments, where wireless links are extremely unstable, this causes numerous retransmissions. Much research has recently focused on routing schemes that guarantee energy-efficient and reliable data transmission. In circumstances without standard routing schemes in wireless sensor networks, however, it is neither practical nor efficient to propose a new routing scheme. To guarantee reliable data transmission in general way, in this paper, we propose Delay-based Reliable Data Transmission (DRDT), a modular approach that offers expansion to existing routing schemes. DRDT offers reliability in transmission through cooperative retransmission by a helper node that overheard the data packet, when the data reception of a receiver node fails due to an unstable link. The helper node is dynamically selected based on the distributed method that uses a delay when the neighbor nodes of the receiver node overhearing the data packet. DRDT reduces the number of retransmissions by considering the link quality with the receiver node. Simulation shows that DRDT improves delivery cost by up to approximately 45% compared to existing schemes.

Applications in wireless sensor networks (WSNs) experience the ¿local minimum phenomenon.¿ This is caused by deployment holes and blocks forwarding process. Recent noteworthy schemes, such as SLGF, Virtual Ellipse, and Virtual Circle, have been proposed to bypass holes. However, they still have a long routing path. In this paper, we introduce a Virtual Convex Polygon approach in which the hole boundary information is transferred to a subset of nodes. After that, the best approximation convex shape, i.e., the virtual convex polygon, is built based on this information. The shorter distance path of the clockwise side and counterclockwise side paths is chosen to combine with a dynamic forwarder selection procedure to construct the final routing path for bypassing the hole. Comprehensive computer simulations show that the proposed scheme significantly reduces the number of hops and the length of the routing path by 34% shorter than SLGF, 29% shorter than Virtual Ellipse, and 20% shorter than Virtual Circle. It also achieves a high delivery ratio and increases the number of data transfers.

Applications in wireless sensor networks (WSNs) experience the energy hole problem, i.e., nodes closer to the base station drain their energy much faster than nodes that are far from the base station; accordingly, the network lifetime is reduced. Recently, Medidi and Zhou have proposed a DDC approach to address the energy hole problem by assigning different duty cycles for nodes in different coronas. However, they do not consider how to balance the energy consumption between nodes in a corona and guarantee high packet delivery. In this paper, we describe TTNS, a novel two tiered network structure approach to prolong the network lifetime. TTNS defines the network as two tiers of coronas and sub-corona slices. With the proposed network structure, we balance energy consumption of nodes not only in every corona, but also in every slice within each corona. Moreover, a high packet delivery to the base station is also achieved by employing our clockwise packet routing method (an add-on algorithm for TTNS). Comprehensive computer simulations show that our schemes, TTNS and CWPR, balance the energy consumption between the nodes in the network; hence, they can extend network lifetime by 19.5% longer than DDC and maintain an average packet delivery ratio up to 87.4%.

In wireless sensor networks, density control is an important technique for prolonging a network's lifetime. To reduce the overall energy consumption, it is desirable to minimize the overlapping sensing area of sensor nodes. In this paper, we study the problem of energy-efficient area coverage by the regular placement of sensors with adjustable sensing and communication ranges. We suggest a more accurate method to estimate efficiency than those currently used for coverage by sensors with adjustable ranges, and propose new density control models that considerably improve coverage using sensors with two sensing ranges. Calculations and extensive simulation show that the new models outperform existing ones in terms of various performance metrics.

In this paper, we propose new spatial query processing algorithms to support Mobile Continuous Nearest Neighbor Query (MCNNQ) in wireless broadcast environments. Each client (object) computes individually the function that best captures the locations of the moving objects, while a server is only responsible for delivering information gathered from the clients. Extensive experiments demonstrate that our location-based data dissemination algorithm significantly out-performs index-based solutions.

Flooding is a basic operation to propagate control messages in most ad hoc routing protocols. Recently, many flooding schemes have been studied to avoid the problems of severe redundancies, contentions, and collisions in traditional implementations. One of the recent efficient approaches is 1HI by Liu et aL, which uses only 1-hop neighbor information. The advantage of this scheme is that it achieves local optimality in term of the number of retransmitting nodes with the time complexity O(nlogn), although considerable redundant transmissions still remain. In this paper, we present a novel flooding algorithm to efficiently reduce the number of retransmitting nodes and to solve the broadcast storm problem in ad hoc networks by using our proposed concept of "2-hop backward information." The most important feature of the proposed algorithm is that it does not require any extra communication overhead other than the exchanging of broadcast storm problem, while maintaining the high deliverability. Comprehensive simulations are performed to show that the proposed scheme significantly reduces the redundant transmissions in 1HI and pure flooding; accordingly it alleviates the contentions and collisions in networks.

A mobile ad-hoc network is a self-configuring network of user devices that are connected by wireless links in infrastructure-less situation. These kinds of networks have many challenges such as unreliable links, lack of scalability, limited resources, etc. and energy consumption is a major issue in designing network protocols because of battery constraints. In on-demand multi-hop routing protocols e.g. AODV and DSR, the route discovery process uses expanding ring search heuristic algorithm for reducing broadcast overhead and saving energy consumption. However, based on our observation we see that there are still some redundant broadcasts of this process that causes overhead, wasted energy, and increased collisions in the network. Here we propose a method which solves the redundant broadcasts of route discovery based on expanding ring search. The performance evaluation results show that by applying the proposed scheme, we can reduce the overhead of expanding ring search based route discovery up to about 20%, and therefore the total energy consumption of AODV is decreased by 15%.

This paper investigates the construction of a multicast tree satisfying Quality of Service (QoS) real-time group communication in a heterogeneous network comprising multiple Mobile Ad-hoc NETworks (MANETs) attached to the backbone Internet. The main objective of our work is to optimize the Delay- and delay Variation Bounded Multicast Tree (DVBMT) problem, which has been proved to be NP-complete. This problem has to satisfy the minimum delay variation and the end-to-end delay within an upper bound. The well-known algorithms solved this problem are the DVMA, the DDVCA, the Cheng's algorithm, and so on. In this paper, we propose an algorithm that outperforms other algorithms in terms of the multicast delay variation in the realistic network environment. The enhancement increases to approximately 3.7%~ 32.9% in terms of that. The time complexity of the proposed algorithm is O(mn2), which is comparable to that of DDVCA.

Indexing provides for selective tuning but suffers from the drawback that, in order to conserve battery power, the client has to wait for and tune to the index segment. In location-aware mobile services (LAMSs), it is important to reduce the query response time since a late query response may contain out-of-date information. In this paper, we present a broadcast-based spatial query processing scheme designed to support nearest neighbor (NN) query processing. With the proposed schemes, broadcast data items are sorted sequentially based on their locations and the clients can selectively tune to the desired data item without the need for an index segment. For the purpose of selective tuning, we present the exponential sequence scheme (ESS) and cluster-based Fibonacci sequence scheme (CFS) schemes. The ESS and CFS schemes attempt to conserve battery power. The performance of our schemes is investigated in relation to various environmental variables such as the distributions of data objects, the average speed of the clients, and the size of the service area. The resulting latency and tuning time are close to the optimum values, as our analysis and simulation results indicate

The development of the efficient quality of service (QoS) routing algorithms in a high speed network environment is very important and, at the same time, a very difficult task due to the need to provide divergent services with QoS requirements. The distributed adaptive routing is the typical routing algorithm that is used in the current Internet. If the parameter we concern is to measure the delay on that link, then the shortest path algorithm obtains the least delay (LD) path. Meanwhile, if the parameter is to measure the link cost, then the shortest path algorithm calculates the least cost (LC) path. The delay constrained least cost (DCLC) path problem has been shown to be NP-hard. The path cost of LD path is relatively more expensive than that of the LC path, and the path delay of LC path is relatively higher than that of the LD path in DCLC problem. In this paper, we investigate the performance of a heuristic algorithm, estimated link selection (ELS) for the DCLC problem with a new parameter which is a probabilistic combination of cost and delay. We have performed empirical evaluation that compares our proposed ELS with the DCUR in various network situations. It significantly contributes to identify the low cost and low delay unicasting path and the performance improvement is up to about 49% in terms of normalized surcharge.

In mobile wireless network efficient location management for tracking and finding mobile users is a critical issue. The traffic for location update can be excessive, especially at the base stations that are near to the location area (LA) boundaries. In this paper we propose a new location update scheme which can significantly reduce the signaling traffic for location update. It is based on the virtual layer approach employing SubMSCs. The virtual layer is laid upon the original layer of LA's such that the mobile terminals moving around the boundary cells of adjacent LA's become to move within a virtual LA. As a result, the location update traffic is substantially reduced compared to overlapping scheme which is the most recent and efficient location update scheme.

While various checkpointing schemes have been widely used to reduce the recovery time when a fault occurs, the problem of evaluating the optimal checkpoint interval that maximizes the availability of the system has been a critical research issue for decades. The evaluation can be done by developing analytical models with restrict assumptions. However, the analytical model has reached its limitations as the checkpointing schemes become complicated. This paper proposes to use stochastic Petri net model for the evaluation and shows the effectiveness of the approach using case studies. The paper develops stochastic Petri net models and shows how to obtain the optimal checkpoint intervals for systems employing two widely used checkpointing schemes: Checkpoint with Rollback Recovery scheme for uniprocessor systems and Primary Site Approach for multiprocessor systems.

VLSI-based processor arrays have been widely used for computation intensive applications such as matrix and graph algorithms. Algorithm-based fault tolerance designs employing various encoding/decoding schemes have been proposed for such systems to effectively tolerate operation time fault. In this paper, we propose an efficient algorithm-based fault tolerance design using the weighted data-check relationship, where the checks are obtained from the weighted data. The relationship is systematically defined as a new (n, k, N/sub w/) Hamming checksum code, where n is the size of the code word, k is the number of information elements in the code word, and N/sub w/ is the number of weights employed, respectively. The proposed design with various weights is evaluated in terms of time and hardware overhead as well as overflow probability and round-off error. Two different schemes employing the (n, k, 2) and (n, k, 3) Hamming checksum code are illustrated using important matrix computations. Comparison with other schemes reveals that the (n, k, 3) Hamming checksum scheme is very efficient, while the hardware overhead is small.

Multicomputer systems achieve high performance by utilizing a number of computing nodes. Recently, by achieving significant reductions in communication delay, the three-dimensional (3D) torus has emerged as a new candidate interconnection topology for message-passing multicomputer systems. In this paper, we propose an efficient processor allocation scheme-scan search scheme-for the 3D torus based on a first-fit approach. The scan search scheme minimizes the average allocation time for an incoming task by effectively manipulating the 3D information on a torus as 2D information using a data structure called the CST (Coverage Status Table). Comprehensive computer simulation reveals that the allocation time of the scan search scheme is always smaller than that of the earlier scheme based on a best-fit approach. The difference gets larger as the input load increases, and it is as much a factor of 3 for high load. To investigate the performance of the proposed scheme in different scheduling environments, we also consider a non-FCFS scheduling policy along with the typical FCFS policy. The allocation time complexity of the scan search scheme is O(LW/sup 2/H/sup 2/). This is significantly smaller than that of the existing scheme which is O(L/sup 4/W/sup 4/H/sup 4/). Here, L, W, and H represent the length, width, and height of 3D torus, respectively.

Multistage interconnection networks (MINs) have been widely used for parallel computer systems, and also recognized as an efficient switching fabric for digital communication. In this paper, we propose a new switching mechanism for MINs called unit step buffering (USB) which significantly improves the network performance. Here each cell is allowed to move only one buffer entry position using short network cycle. The proposed USB scheme is compared to the traditional scheme by analytical modeling and computer simulation. They reveal that throughput and delay are improved about 60%-80% for practical size MINs with reasonable traffic in the asynchronous transfer mode (ATM) switching environment. Improvement on parallel computer systems with larger size packets is more significant at about 100%. More importantly, the scheme does not require any additional hardware or operational overhead.

The task scheduling policy and the processor allocation scheme affect the system performance significantly. In this paper, we propose an efficient processor allocation scheme for 3D mesh interconnection network with a simple FIFO scheduling policy. Complexity analysis shows that the allocation and deallocation of the scheme are O(LWH/sup 2/) and O(LH), respectively, which are better than earlier schemes. Comprehensive computer simulation shows that the average allocation time of the proposed scheme is improved up to about 85% compared to the best earlier 3D approach.

Scheduling parallel tasks represented as a Directed Acyclic Graph (DAG), on a multiprocessor system has been an important research area in the past decades. One of the critical aspects of a class of scheduling algorithms, called "List Scheduling", is how to decide which task is to be scheduled next. This is achieved by assigning priorities to the nodes or the edges of the input DAG, and thus the task with the highest priority will be scheduled next. This paper proposes a low complexity scheduling algorithm to improve the priority node selection criteria in list scheduling algorithms. The worst case performance of the proposed algorithm is analyzed for general input DAGs. Also, the worst case performance and the optimality conditions are obtained for free structured input DAGs. The performance comparison study shows that the proposed algorithm outperforms existing scheduling algorithms especially for input DAGs with high communication overheads. The performance improvement over existing algorithms becomes larger as the input DAG becomes more dense and the level of parallelism in the DAG is increased.