Text-to-image diffusion models may generate harmful or copyrighted content, motivating research on concept erasure. However, existing approaches primarily focus on erasing concepts from text prompts, overlooking other input modalities that are increasingly critical in real-world applications such as image editing and personalized generation. These modalities can become attack surfaces, where erased concepts re-emerge despite defenses. To bridge this gap, we introduce M-ErasureBench, a novel multimodal evaluation framework that systematically benchmarks concept erasure methods across three input modalities: text prompts, learned embeddings, and inverted latents. For the latter two, we evaluate both white-box and black-box access, yielding five evaluation scenarios. Our analysis shows that existing methods achieve strong erasure performance against text prompts but largely fail under learned embeddings and inverted latents, with Concept Reproduction Rate (CRR) exceeding 90% in the white-box setting. To address these vulnerabilities, we propose IRECE (Inference-time Robustness Enhancement for Concept Erasure), a plug-and-play module that localizes target concepts via cross-attention and perturbs the associated latents during denoising. Experiments demonstrate that IRECE consistently restores robustness, reducing CRR by up to 40% under the most challenging white-box latent inversion scenario, while preserving visual quality. To the best of our knowledge, M-ErasureBench provides the first comprehensive benchmark of concept erasure beyond text prompts. Together with IRECE, our benchmark offers practical safeguards for building more reliable protective generative models.

Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. However, the ease of text-based image editing introduces significant risks, such as malicious editing for scams or intellectual property infringement. Previous works have attempted to safeguard images from diffusion-based editing by adding imperceptible perturbations. These methods are costly and specifically target prevalent Latent Diffusion Models (LDMs), while Pixel-domain Diffusion Models (PDMs) remain largely unexplored and robust against such attacks. Our work addresses this gap by proposing a novel attack framework, AtkPDM. AtkPDM is mainly composed of a feature representation attacking loss that exploits vulnerabilities in denoising UNets and a latent optimization strategy to enhance the naturalness of adversarial images. Extensive experiments demonstrate the effectiveness of our approach in attacking dominant PDM-based editing methods (e.g., SDEdit) while maintaining reasonable fidelity and robustness against common defense methods. Additionally, our framework is extensible to LDMs, achieving comparable performance to existing approaches.

With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabilities are usually sacrificed and thus hinder their practical uses in real-world scenarios. To address this issue, we propose a novel training-free Diffusion-based QR Code generator (DiffQRCoder) to effectively craft both scannable and visually pleasing QR codes. The proposed approach introduces Scanning-Robust Perceptual Guidance (SRPG), a new diffusion guidance for Diffusion Models to guarantee the generated aesthetic codes to obey the ground-truth QR codes while maintaining their attractiveness during the denoising process. Additionally, we present another post-processing technique, Scanning Robust Manifold Projected Gradient Descent (SR-MPGD), to further enhance their scanning robustness through iterative latent space optimization. With extensive experiments, the results demonstrate that our approach not only outperforms other compared methods in Scanning Success Rate (SSR) with better or comparable CLIP aesthetic score (CLIP-aes.) but also significantly improves the SSR of the ControlNet-only approach from 60% to 99%. The subjective evaluation indicates that our approach achieves promising visual attractiveness to users as well. Finally, even with different scanning angles and the most rigorous error tolerance settings, our approach robustly achieves over 95% SSR, demonstrating its capability for real-world applications. Our project page is available at https://jwliao1209.github.io/DiffQRCoder.

Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and ensemble learning methods improve the poor accuracy of the minority class, these methods are limited by overfitting or cost parameters that are difficult to decide. This paper proposes a hybrid approach with dimension reduction that consists of data block construction, dimensionality reduction, and ensemble learning with deep neural network classifiers. The performance is evaluated on eight imbalanced public datasets in terms of recall, G-mean, AUC, F-measure, and balanced accuracy. The results show that the proposed model outperforms state-of-the-art methods.

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (ICD) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records. Inspired by the click-through rate (CTR) problem in recommendation systems, where future user behavior is predicted based on their past consumption records, we address these challenges with a multimodal attention network for dementia (MAND), which incorporates an ICD code embedding layer and multihead self-attention to encode ICD codes and capture interactions among diseases. Additionally, we investigate the applicability of several CTR methods to the dementia prediction problem. MAND achieves an AUC of 0.9010, surpassing traditional CTR models and demonstrating its effectiveness. The highly flexible pipelined design allows for module replacement to meet specific requirements. Furthermore, the analysis of attention scores reveals diseases highly correlated with dementia, aligning with prior research and emphasizing the interpretability of the model. This research deepens our understanding of the diseases associated with dementia, and the accurate prediction provided can serve as an early warning for dementia occurrence, aiding in its prevention.

The sixth-generation (6G) wireless communication networks are anticipated to combine terrestrial, aerial, and marine communications into a dependable, fast network that could handle many devices with ultra-low latency requirements. Reconfigurable intelligent surfaces (RIS)-aided millimeter-wave massive MIMO communication systems could increase wireless link quality by providing passive beamforming gain via low-cost reflecting components. On the other hand, the power consumption and cost could be reduced much by applying a hybrid precoding architecture, which combines digital and analog precoding modules. However, using RIS to solve the problem of hybrid precoding is difficult because how to Figure out reflecting coefficients without beam training overhead or large channel estimation is a challenging issue. In this work, we propose a novel hybrid precoding architecture based on geometric mean decomposition (GMD) and jointly consider the design of the Long ShortTerm Memory (LSTM) autoencoder scheme. That is, we adopt GMD for diminishing the computational complexity and enhancing the hybrid precoding performance. Since the encoder-decoder design of autoencoder serves as a dimensionality reduction strategy, the LSTM autoencoder are able to capture the temporal and spatial distribution of the sequential data by using the LSTM models sequenhal and feature extraction capabilities. Numerical results show that our proposed algorithm could significantly improve the RIS-aided millimeter-wave MIMO communication systems performance compared with previous works.

We investigate a novel way to integrate visual SLAM and lidar SLAM. Instead of enhancing visual odometry via lidar depths or using visual odometry as the motion initial guess of lidar odometry, we propose tightly-coupled visual-lidar SLAM (TVL-SLAM), in which the visual and lidar frontend are run independently and which incorporates all of the visual and lidar measurements in the backend optimizations. To achieve large-scale bundle adjustments in TVL-SLAM, we focus on accurate and efficient lidar residual compression. The visual-lidar SLAM system implemented in this work is based on the open-source ORB-SLAM2 and a lidar SLAM method with average performance, whereas the resulting visual-lidar SLAM clearly outperforms existing visual/lidar SLAM approaches, achieving 0.52% error on KITTI training sequences and 0.56% error on testing sequences.

The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

With the programmability of software defined networking (SDN), it is convenient to manage network through the master and slave controllers that also provide the failover mechanism. To achieve load balancing among SDN controllers and low response time, we should dynamically assign controllers under the bandwidth constraint, so the bandwidth consumption is important. In this paper, we formulate the bandwidth consumption as mix integer linear programming model and figure out that it is NP-hard. Thus, we propose a two-phase algorithm to handle it. In the first phase, we use k-means clustering algorithm to avoid achieving local minimum and try to maximize isolating failure effect. In the second phase, we use the output of first phase as initial assignment and utilize coalitional game to further improve the result. The simulation result shows that our proposed algorithm can not only get closer to optimal result but also isolate failure effect.

Millimeter Wave (Mm Wave) massive multiple-input multiple-output (MIMO) has become an advantageous technology for gigabit-per-second data transmission in 5G wireless communication. To achieve low-cost and energy-efficient hardware components, one-bit quantized massive MIMO systems have been proposed for the receiver hardware architecture. The main focus of this work is leveraging the advantages of a state of the art one-bit quantized massive MIMO system for design of an adaptive-bit massive MIMO system. Hence, in this work, by leveraging the benefits of variational approximate message passing (VAMP), a novel MMSE-based VAMP algorithm is proposed for the adaptive-bit quantized massive MIMO system. That is, two novel modules, i.e., an adaptive ADC bit allocation method and an MMSE-based VAMP, are proposed for mm Wave communications of the hybrid MIMO receiver architecture. With the MMSE-based VAMP, our adaptive ADC bit allocation method is able to decrease the quantization of signals distortion by improving the flexible resolutions of ADC. Through simulations, compared with existing works, our proposed adaptive ADC bit allocation algorithm, together with MMSE-based VAMP, is able to achieve higher capacity, sum rate, and energy efficiency in most communication architectures.

Virtual reality (VR) and augmented reality (AR) are expected to change people's daily life in the future, but it still faces many challenges. New advanced coding techniques like depth image based rendering (DIBR) mitigate the need to complete content by synthesizing the scene with nearby views. However, due to the limitation of wireless spectrum, delivering VR/AR content to many people doing some activities (e.g., watching sport in a stadium or seeing an exhibition in a museum) in mobile hotspot areas is still very challenging in terms of available bandwidth. In this work, we analyze how to multicast multi-view VR/AR 3D multimedia streams over multiple wireless access points (APs) to multiple users, and then propose a multi-view allocation (MVA) algorithm to improve the delivered content quality. The simulation results show that our MVA algorithm can achieve about 90% performance of the optimal solution and outperform non-DIBR and SSA model, by 48% and 25%, respectively.

Recently distributed multiple SDN controller architecture is proposed to improve the scalability of SDN architecture. Unfortunately, the state-of-art SDN controller failover mechanism, i.e., switch leader competition, did not consider the characteristics of data center network and the time cost, resource usage, and load balancing after failure. In this paper, we propose a temporal-based load adaptive SDN controller failover mechanism (TLACF) based on the temporal pattern of traffic. The results demonstrate the proposed TLACF can save up to 46% time cost of failover and 66% memory usage for state backup.

In today`s networks, middleboxes, typically deployed as standalone devices with no standardized access, play an important role in providing services such as firewalls and NATs as well as load balancing. These networks resort to Software-Defined Networking (SDN) for management that can provide a centralized, programmable environment to direct traffic through a desired service chain. However, even if the SDN Controller is able to determine the best path to a certain middlebox, not having prior knowledge of its policies may lead to traffic bottlenecks, degrading overall network performance. By jointly considering the characteristics of middleboxes and SDN, we design a Lightweight, cost-effective, and middlebox policy-aware routing method to address this challenge. That is, a middlebox-translator module is added to the SDN architecture to help middleboxes give the controller routing“hints” in the REST (Representational State Transfer) API format so that better routing decisions can be made. A performance study using emulations of Mininet, shows that, compared to existing methods, our middlebox-aware design results in better performance in SDN-enabled systems, e.g., greater network bandwidth savings in firewall systems, or reduced response time as well as higher throughput in load-balancing systems.

With the rapid development of V2X communications, how to guarantee per-vehicle rate and robustness of V2X communications has become important issue for the intelligent transportation systems. When there is distortion in CSI exchange under a real (e.g., noisy) environment, the performance of the precoder feedback method will seriously degrade due to transmission latency and quantization error. In this work, we propose Finsler manifolds codebook feedback scheme for V2X massive MIMO systems, where the vehicles exchange their CSI information via V2V communications, estimate the direction from a propagating wave in the precoder of antenna transceivers, and transmit their CSIs back to the Roadside Unit (RSU). That is, with the minimizing the iterative weighted minimum mean squared estimation (IWMMSE) of the received signals, we could cope with the optimization problem of the precoder feedback scheme on Finsler manifolds for maximizing the received signal power. Moreover, we are able to properly manage the optimal precoder bit allocation in mmWave massive MIMO systems. Simulation results show that, with the IWMMSE-based estimation, our Finsler manifolds codebook feedback scheme outperforms existing approaches in terms of per vehicle rate as well as the interference mitigation, i.e., our massive MIMO system could obtain higher adaptability and stability for V2X communications.

The work of cooperative perception realized by mmWave Vehicle to everything (V2X) [3] has pointed out that vehicles require much network bandwidth to exchange sensor information for safe automated driving; e.g., they require higher than 1Gbps V2V bandwidth to ensure safe velocity of 70 km/h. On the other hand, some research works have shown that channel state information (CSI) feedback is able to mitigate interference for 5G frequency division duplexing (FDD) mmWave massive MIMO systems. Existing approaches either do not consider the characteristics of FDD mmWave V2X systems or assume all channels are ideal, i.e., uncorrelated and identical. To achieve higher throughput and low overhead for FDD mmWave V2X systems, there are two major issues: (1) how to acquire an accurate CSI under the environment of non-identical channels, and (2) how to effectively use such information to improve overall system throughput. Our idea is to jointly consider the characteristics of the angle of arrival (AoA) method and K̈hler manifolds to design of codebook feedback approach for mmWave V2X massive systems. That is, to get an accurate CSI, we leverage the measurement method with the idea of the AoA approach. With this AoA measurement, we propose a novel K̈hler manifolds codebook feedback approach and determine an optimal bit allotment over each mmWave V2X link for achieving better interference reduction as well as lower overhead for V2X CSI exchange. In addition, our analytic results validate that proposed AoA K̈hler manifolds codebook feedback scheme is more adequate than the traditional CSI feedback with regard to interference reduction. Simulations show that the proposed AoA K̈hler manifolds codebook feedback scheme can significantly improve the vehicle rate and reduce the overhead for V2V CSI exchange.

The millimeter wave (mmWave) technology is considered as the potential candidate for high speed telecommunications service in 5G wireless communication, as it offers a ten times larger spectrum than existing cellular systems. The generalized spatial modulation (GenSM) aided millimeter wave multiple input multiple-output (mm-wave MIMO) concept have attracted substantial research interest, as it could lead to significant performance enhancement of mm-wave MIMO while maintaining the antennas with reduced number of active chains. However, current solvers either get stuck in a local minimum or have much computational complexity of GenSM-aided mm-wave MIMO schemes because the non-convex nature of the hybrid precoding design incurs significant performance degradation. To address this problem, we employ the Riemannian Optimization (RO) algorithms for the design of a hybrid precoder for GenSM-aided mm-wave MIMOs in order to enhance the spectral efficiency (SE). That is, we first re-design the hybrid precoding structures, i.e., by integrating the RF chain power constraints into the objective function; this allows the original optimized problem with constraints to be transformed into an unconstrained optimization problem on a nonlinear search space. Next, by considering the characteristics of a digital precoder, we are able to construct a proper manifold, in which we could effectively apply the RO iterative gradient descent method for computing a better solution. Our simulation results show that the proposed RO-based hybrid precoder is able to (a) outperform traditional GenSM-aided mm-wave MIMOs schemes, and (b) achieve comparable SE performance, when compared with modern mm-wave MIMO schemes.

With the recent advances in software-defined networking (SDN), the multi-tenant data centers provide more efficient and flexible cloud platform to their subscribers. However, as the number, scale, and diversity of distributed denial-of-service (DDoS) attack is dramatically escalated in recent years, the availability of those platforms is still under risk. We note that the state-of-art DDoS protection architectures did not fully utilize the potential of SDN and network function virtualization (NFV) to mitigate the impact of attack traffic on data center network. Therefore, in this paper, we exploit the flexibility of SDN and NFV to propose FlexProtect, a flexible distributed DDoS protection architecture for multi-tenant data centers. In FlexProtect, the detection virtual network functions (VNFs) are placed near the service provider and the defense VNFs are placed near the edge routers for effectively detection and avoid internal bandwidth consumption, respectively. Based on the architecture, we then propose FP-SYN, an anti-spoofing SYN flood protection mechanism. The emulation and simulation results with real-world data demonstrates that, compared with the traditional approach, the proposed architecture can significantly reduce 46% of the additional routing path and save 60% internal bandwidth consumption. Moreover, the proposed detection mechanism for anti-spoofing can achieve 98% accuracy.

The success of Deep Neural Networks (DNNs) has created significant interest in the development of software tools, hardware architectures, and cloud systems to meet the huge computational demand of their training jobs. A common approach to speeding up an individual job is to distribute training data and computation among multiple nodes, periodically exchanging intermediate results. In this paper, we address two important problems for the application of this strategy to large-scale clusters and multiple, heterogeneous jobs. First, we propose and validate a queueing model to estimate the throughput of a training job as a function of the number of nodes assigned to the job; this model targets asynchronous Stochastic Gradient Descent (SGD), a popular strategy for distributed training, and requires only data from quick, two-node profiling in addition to job characteristics (number of requested training epochs, mini-batch size, size of DNN parameters, assigned bandwidth). Throughput estimations are then used to explore several classes of scheduling heuristics to reduce response time in a scenario where heterogeneous jobs are continuously submitted to a large-scale cluster. These scheduling algorithms dynamically select which jobs to run and how many nodes to assign to each job, based on different trade-offs between service time reduction and efficiency (e.g., speedup per additional node). Heuristics are evaluated through extensive simulations of realistic DNN workloads, also investigating the effects of early termination, a common scenario for DNN training jobs.

Effective channel state information (CSI) feedback design is difficult for massive multiple-input multiple-output (MIMO) downlink transmissions in 5G frequency division duplexing (FDD) bands, where partial CSI could be directly exchanged between users through device-to-device (D2D) communications. In this work, we propose Direction of Arrival (DoA) adaptive codebook feedback, where the users exchange their CSI knowledge through D2D communications, estimate the precoder, and transmit it back to the base station (BS). That is, we first formulate an optimization problem of the feedback and precoding strategy on a Grassmann manifold. Using that, we are able to determine an optimal bit allocation of the precoder and the throughput of mmWave massive MIMO systems. Using simulations, we show that, compared with existing schemes, our proposed DoA adaptive codebook feedback scheme is able to achieve much higher throughput, and improve robustness and compatibility.

Border Gateway Protocol (BGP) has been the de facto inter-domain routing protocol since it was introduced. Its destination-based routing nature, which is not able to choose a specific end-to-end AS-level route, might overload some popular peering (or inter-AS) links, while making some others underused. This could result in congestion and cause service disruption. To cope with the issue of destination-based routing of BGP, we resort to Software-Defined Networking (SDN) techniques to propose Effective Tunnel-based Multi-path BGP (ETMP-BGP), which is able to obtain the whole control of tunnel-based multi-path BGP routing in terms of AS-level routing. In ETMP-BGP, destinations provide the feedback of path quality to the source. This feedback is used to detect congestions on AS-level routes and adjust such routes accordingly in a per-source basis. That is, ETMP-BGP with SDN is able to get the AS-level global view of Internet and choose a better AS-level end-to-end BGP routing. Through simulations, our results show that ETMP-BGP could mitigate the AS-level congestions and outperforms existing BGP schemes.

Search with privacy-preserving guarantee has been a critical issue for commercial cloud storage. Data owners outsource a large number of encrypted sensitive data to cloud and allow data users to search and retrieve encrypted data in cloud. Recently, Cao et al. [1] proposed a scheme to enable multi-keyword ranked search over encrypted data. In this paper, we focus on practical factors and incremental update in cloud. We utilize document replication and partition with one-to-many order preserving encryption to achieve privacy-preserving guarantees. We also introduce an index structure to provide efficient incremental update. Under a reasonable practical situation, the proposed scheme is more efficient than previous works and has lower communication overhead.

In recent years, wireless network structures and mechanisms have been proposed to enhance the efficiency of communications. Regarding the upper-layer of protocol stacks, wireless transmission control protocols (WTCPs) are proposed to deal with transmission control protocol (TCP) performance problems on wireless networks. However, some new lower-layer solutions, e.g., cognitive radio (CR) systems, cause new throughput decay problems to TCPs, which existing WTCPs do not solve. In this paper, we study throughput decay issues in CR environments and discuss three events that cause these problems. Three event handlers are proposed to improve TCP throughput. The proposed solutions are designed by numerical analysis, and performance improvement is verified by NS2. We propose an event-driven CR-TCP that combines these event handlers, which can be combined with state-of-art CR-TCP solutions. Simulation results and analysis are presented to demonstrate the effectiveness of the proposed solution.

Allocation of wireless spectrum and transmission power for video multicast in a two-tier femtocell network is a challenging problem because of network dynamics, variable video bit-rates, and heterogeneous clients who watch various videos as well as are at different locations. Since femtocells provide coverage using typical cellular system air interface, the co-channel interference may significantly limit the capacity improvement in this two-tier network. Hence, the focus of this work is to figure out an effective way to cope with the interference issue by allocating system resources (sub-channels and transmission power) efficiently for video multicast users. Prior works apply channel assignment or power control schemes by considering only characteristics of underlying wireless resources. We, however, note that for a video multicast user, the user perceived video quality is associated with not only the wireless channel condition but also the video content. Motivated by this observation, we propose a content-aware spectrum and power allocation scheme (CSPA), which considers both characteristics of underlying wireless resources and video contents. Through simulation, results show that CSPA is able to enhance the users' watching video quality than other existing schemes; and the gap between CSPA and other schemes becomes larger when all users' contents become more diverse.

Designing a lifetime-maximization routing in wireless sensor networks poses a great challenge mainly due to unreliable wireless links and limited power supply. Recently, two natural advantages of opportunistic routing, i.e., path diversity and the improvement of transmission reliability, are exploited to develop a lifetime-extended opportunistic routing for wireless sensor networks. Besides, asynchronous sleep-wake scheduling is an effective mechanism to reduce energy consumption by appropriately arranging sensor nodes to sleep. Hence, in this paper, we propose a joint design of asynchronous sleep-wake schedules and opportunistic routing, called ASSORT, to maximize the network lifetime. Simulation results show that ASSORT effectively achieves network lifetime extension compared with other routing schemes.

Flexible channels enable Access Points (AP) and clients to determine channel widths and their center frequencies. The key challenge in flexible channels is how to determine proper channel width and center frequency for each link demands such that the overall system throughput could be improved. In this work, we introduce a distributed flexible channel assignment algorithm, DFCA. Different from prior researches, DFCA does link-based channel assignment instead of AP-based or packet-based. This is because that the packet-based method could incur heavy overhead, while the AP-based method is not able to determine the interfering relation between links precisely. In addition, the transmission weight of a link is proposed; with the help of the transmission weight, channel width, center frequency and interference relation to neighboring nodes, DFCA is able to accurately predict that link throughput prior to the channel assignment. Results show that DFCA could outperform traditional fixed channel assignment approaches and the AP-based flexible channel assignment algorithm. Even compared with FLUID[1], which is a packet-based channel assignment approach with assist of a central controller, DFCA is able to achieve comparable (at least 93%) throughput by using only local information rather than global knowledge.

Mobile social networks (MSNs) have attracted much attention for the past few years. Due to non-deterministic social contact and intermittent as well as asymmetric connectivity in MSNs, recent research works on MSNs only focus on unicast or multicast protocols for speeding up object dissemination. Unlike previous works, the focus of this paper is on how to design an efficient (multimedia) object retrieval scheme for MSNs. The key challenge for the object retrieval in MSNs is that we need to deal with not only the connectivity issue in MSNs, but also the heterogeneous objects owned by different users. Our idea is that, given a query object, we jointly consider the user preference probability and the characteristics of the round-trip object retrieval to design the object retrieval scheme. Since multimedia objects are usually associated with multiple attributes, we are able to characterize users by the objects they hold, and then compute the preference probability of the targeted object for each user. Next, we divide the object retrieval scenario in MSNs into two phases: the searching phase and the returning phase. When the query is in searching phase, the relaying user needs to jointly consider the successful probability in the searching phase as well as in the returning phase. In the returning phase, the relaying user just considers the probability of returning the object back to source node. In addition, we also develop a discrete-time analytic model to predict the node delivery probability and use it for the relay node selection. A performance study, via simulations based on MIT Reality and Last.fm music trace, illustrates that our proposed schemes are effective in object retrieval on MSNs.

Adaptation of modulation and transmission bit-rates for video multicast in a multirate wireless network is a challenging problem because of network dynamics, variable video bit-rates, and heterogeneous clients who may expect differentiated video qualities. Prior work on the leader-based schemes selects the transmission bit-rate that provides reliable transmission for the node that experiences the worst channel condition. However, this may penalize other nodes that can achieve a higher throughput by receiving at a higher rate. In this work, we investigate a rate-adaptive video multicast scheme that can provide heterogeneous clients differentiated visual qualities matching their channel conditions. We first propose a rate scheduling model that selects the optimal transmission bit-rate for each video frame to maximize the total visual quality for a multicast group subject to the minimum-visual-quality-guaranteed constraint. We then present a practical and easy-to-implement protocol, called QDM, which constructs a cluster-based structure to characterize node heterogeneity and adapts the transmission bit-rate to network dynamics based on video quality perceived by the representative cluster heads. Since QDM selects the rate by a sample-based technique, it is suitable for real-time streaming even without any preprocess. We show that QDM can adapt to network dynamics and variable video-bit rates efficiently, and produce a gain of 2-5 dB in terms of the average video quality as compared to the leader-based approach.

Since data in underwater sensor networks (UWSNs) is transmitted by acoustic signals, the characteristics of a UWSN are different from those of a terrestrial sensor network. Specifically, due to the high propagation delay of acoustic signals in UWSNs, referred as spatial-temporal uncertainty, current terrestrial MAC schemes do not work well in UWSNs. Hence, we consider spatial-temporal uncertainty in the design of an energy-efficient TDMA-based MAC protocol for UWSNs. We first translate the TDMA-based scheduling problem in UWSNs into a special vertex-coloring problem in the context of a spatial-temporal conflict graph (ST-CG) that describes explicitly the conflict delays among transmission links. With the help of the ST-CG, we propose two novel heuristic approaches: 1) the traffic-based one-step trial approach (TOTA) to solve the coloring problem in a centralized fashion; and for scalability, 2) the distributed traffic-based one-step trial approach (DTOTA) to assign the data schedule for tree-based routing structures in a distributed manner. In addition, a mixed integer linear programming (MILP) model is derived to obtain a theoretical bound for the TDMA-based scheduling problem in UWSNs. Finally, a comprehensive performance study is presented, showing that both TOTA and DTOTA guarantee collision-free transmission. They thus outperform existing MAC schemes such as S-MAC, ECDiG, and T-Lohi in terms of network throughput and energy consumption.

ABSTRACT The utilization of limited energy in wireless sensor networks (WSNs) is the critical concern, whereas the effectiveness of routing mechanisms substantially influence energy usage. We notice that two common issues in existing specific routing schemes for WSNs are that (i) a path may traverse through a specific set of sensors, draining out their energy quickly and (ii) packet retransmissions over unreliable links may consume energy significantly. In this paper, we develop an energy‐efficient routing scheme (called EFFORT) to maximize the amount of data gathered in WSNs before the end of network lifetime. By exploiting two natural advantages of opportunistic routing, that is, the path diversity and the improvement of transmission reliability, we propose a new metric that enables each sensor to determine a suitable set of forwarders as well as their relay priorities. We then present EFFORT, a routing protocol that utilizes energy efficiently and prolongs network lifetime based on the proposed routing metric. Simulation results show that EFFORT significantly outperforms other routing protocols. Copyright © 2011 John Wiley & Sons, Ltd.

As mobile devices have become more ubiquitous, mobile users increasingly expect to utilize proximity-based connectivity, e.g., WiFi and Bluetooth, to opportunistically share multimedia content based on their personal preferences. However, many previous studies investigate content dissemination protocols that distribute a single object to as many users in an opportunistic mobile social network as possible without considering user preference. In this paper, we propose PrefCast, a preference-aware content dissemination protocol that targets on maximally satisfying user preference for content objects. Due to non-persistent connectivity between users in a mobile social network, when a user meets neighboring users for a limited contact duration, it needs to efficiently disseminate a suitable set of objects that can bring possible future contacts a high utility (the quantitative metric of preference satisfaction). We formulate such a problem as a maximum-utility forwarding model, and propose an algorithm that enables each user to predict how much utility it can contribute to future contacts and solve its optimal forwarding schedule in a distributed manner. Our trace-based evaluation shows that PrefCast can produce a 18.5% and 25.2% higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively.

As mobile devices, such as smart phones and tablet PCs, become more and more ubiquitous, it is increasingly popular for users to watch online video streaming on their mobile devices. Because of the nature of wireless broadcast, a wireless router (e.g., an access point or cellular tower) can retrieve video streams from the video server and then efficiently multicast them via wireless medium to multiple clients who are interested in those videos. However, clients could locate in different positions and therefore experience various wireless channel conditions. In addition, clients might be interested in different video content so that different video clips could have various popularity. The heterogeneity in wireless conditions and user interests makes bandwidth allocation in a wireless router for video multicast a challenging problem. In this paper, we propose a marginal-utility-based video multicast scheme that takes variant channel qualities and heterogeneous user interests into account. Our evaluation shows that, compared to interest-oblivious multicast, the proposed interest-aware approach can improve the average visual quality by up to 9 dB.

Peer-to-Peer (P2P) storage systems are cost-effective and reliable platforms that enable users to share their storage to support variant emerging applications, such as peer-to-peer social networks and distributed backup systems. Because different users have heterogeneous online characteristics and bandwidth capabilities, how to replicate data at suitable peers has become an important issue to ensure that users can access any replica with a high probability. Previous work on data replication in a P2P storage system only considers online characteristic of each user, and aims at increasing data availability. However, we notice that, without considering the bandwidth capability of each user, a system might replicate popular data at a user who has a long online duration but does not have enough bandwidth capability to support all the requests. Therefore, in this work, we propose a swap-based replication scheme that jointly considers online characteristic, data popularity and bandwidth capability to improve not only data availability, but also access probability for each data item.

During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism in mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy (i.e. let the chunks with most distortion-rate efficiency get scheduled first) in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. In this paper, we first identify the unique problem and understand the rationale behind. After that, a dynamic strategy-switching approach that combines the advantages of random and importance-first scheduling strategy is proposed. Simulation results indicate that compared with existing approaches our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.

Because of marketing demand of user-centric video dissemination, promoting video content via interactive distribution platforms has received considerable attention recently. In addition, the preferences of clients are heterogenous and dynamic, effectively utilizing the network bandwidth to distribute user-centric content becomes a challenging problem, especially when the number of users in the systems scale up. Therefore, we propose a P2P-based distribution platform, called Bulldozer, which enable clients to cooperatively propagate user-centric video content in a large scale environment, and better utilizes peers upload bandwidth to share the server traffic load. Bulldozer consists of two components: 1) Clip Scheduling phase schedules the playing order of videos for each individual user, and 2) Bandwidth Allocation phase allocates users upload bandwidths to distribute videos among clients. Finally, simulation results show that Bulldozer can effectively reduce the peak traffic load of the server.

During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism for mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. In this work, we propose a dynamic strategy-switching approach that combines the advantages of random scheduling and importance first scheduling to deal with the problem. Simulation results indicate that our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.

In this paper, we propose an intersection graph-based vehicular ad hoc network (VANET) architecture. Using the available electronic MAP and historical traffic statistics from public traffic databases, we create an intersection graph (IG) consisting of all connected road segments, which have shorter average inter-vehicle distances than the wireless transmission range, as its edges and intersections of these road segments as its vertices. We then calculate the least cost routing path in the IG. Hence, the source vehicle leverages the proposed IG and IG bypass routing protocols to greedily forward unicast packets to the destination vehicle via each intermediate intersection on the least cost IG path. Further, we also propose the IG routing path recovery process to handle the broken IG path in real-time. Finally, we execute NS2 simulations to exhibit that the IG and IG bypass routing protocols significantly outperform four well-known VANET ones in terms of the average packet delivery ratio, end-to-end delay and hop count.

Modern mobile devices have become an important part of our daily life but the performance of multimedia applications still suffers from the constrained energy supply and communication bandwidth of the mobile devices. In this work, we develop an energy-efficient streaming system for mobile hotspots to achieve better Quality-of-Experience. Our main idea is (a) to avoid redundant 3G transmissions as well as reduce the usage of 3G links for those low residual-energy users, and (b) to enable nearby mobile users cooperatively to share the downloaded data via short-range interfaces. The experiment results shows our scheme can improve the system lifetime by 27%, and provide better throughput as well as lower loss rate than conversional 3G systems do.

During recent years, the success of live swarm-based P2P streaming system has been witnessed. Nevertheless, how to design an effective mechanism for mitigating video quality degradation in a lossy network environment is still not thoroughly resolved yet. Unlike conventional client-server paradigm, there is data availability problem in swarm-based P2P streaming system. If we directly conduct the importance-first scheduling strategy (i.e. let the chunks with most distortion-rate efficiency get scheduled first) in swarm-based P2P streaming system, the serious content bottleneck for low priority chunks occurs, particularly when the population size is large. To cope with the above issue, in this work, we propose a dynamic strategy-switching approach that combines the advantages of random scheduling and importance-first scheduling. Our simulation results indicate that compared with existing approaches our approach not only provides better scheduling efficiency, but also is scalable even if population size is large.

Lifetime-maximization is the critical concern for wireless sensor networks (WSNs). We notice that two common issues in existing routing schemes for WSNs are that (1) a path may traverse through a fixed set of sensors, draining out their energy quickly, and (2) packet retransmissions over an unreliable link of any fixed-path may consume energy significantly. In this paper, we exploit two natural advantages of opportunistic routing, i.e., path diversity and the improvement of transmission reliability, to develop a distributed routing scheme (called EFFORT) for prolonging the network-lifetime of a WSN. Specifically, a new metric is proposed to assist each sensor in determining a suitable set of forwarders as well as their priorities and, thus, enables EFFORT to extend the network-lifetime. Simulation results show that EFFORT effectively achieves network-lifetime extension compared to other routing protocols.

The pervasive use of Peer-to-Peer (P2P) systems and the growing demand on personalization for consumers has made future business focus on the niche market instead of the mass market. The recommender system, which is able to timely select data of the interest to each individual user, has become the key to any successful business. However, currently most recommendation systems are based on a centralized architecture; nonetheless, they are not suitable for P2P environments. In this article, we propose a distributed semantic P2P overlay, which can provide music search and recommendation services by considering both of user preference and diversity of interests. We then propose a 3C hybrid recommendation procedure that adapts three traditional filtering techniques to fitting the requirements in a distributed semantic overlay. First, we choose a set of proper meta-data to represent a music object and use them to construct the characteristic-vector-based content filter. Second, a dominant attribute, which is one of the attributes in the characteristic vector of a music object, is used to build the profile of a peer. With the idea of the social network, a P2P profile-based collaborative filter is proposed. Finally, we explore the item-to-item relationship to construct a history-based cooperative filter. We use simulations and a real database called Audio Scrobbler, which tracks users' listening habits, to evaluate the performance of the recommendation system. The results demonstrate the effectiveness of the proposed approach compared with existing recommendation systems.

Recently, much attention has been paid to the problem of how to efficiently utilize multiple orthogonal channels and communication radios to enhance the aggregate throughput of a Wireless Mesh Network (WMN). Most of prior works on the channel assignment problem assume that all transmission links can only transmit at the base rate and do not consider the presence of multiple bit- rates. However, in a multi-rate environment, the achievable throughput of a high-rate link is critically affected by neighboring low-rate links when they share the same channel. This problem is called performance anomaly. To cope with the problem, we propose a Rate-Loss function, which is a quantitative metric used to evaluate how critical the performance anomaly problem occurred in each orthogonal channel. In addition, we present a distributed Rate- Loss based Channel Assignment (RL-CA), which enables each router to select channels suffering from the slightest performance anomaly problem in a distributed manner. The simulation results show that RL-CA outperforms existing schemes and can adapt to network dynamics efficiently.

Increasingly, peer-to-peer (P2P) network users expect to be able to search objects by semantic attributes based on their preferences for multimedia content. Partial match search (i.e., search through the use of multimedia content semantic information) has become an essential service in P2P systems. In this paper, we propose SocioNet, a social-based overlay that clusters peers based on their preference relationships as a small-world network. In SocioNet, peers mimic how people form a social network and how they query, by preference, their friends or acquaintances. Hence, SocioNet benefits from two desirable features of a social network: interest-based clustering and small-world properties (i.e., high cluster coefficient among all peers yet short path lengths between any two peers). To realize an interest-based small-world SocioNet, we also investigate the following practical design issues: 1) similarity estimation: we define a quantifiable similarity measure that enables clustering of similar peers in SocioNet; 2) distributed small-world overlay adaptation: peers maintain a small-world overlay under network dynamics; and 3) query strategy under the small-world overlay: we analyze appropriate settings for the Time-to-Live (TTL) value, for TTL-limited flooding, that provides a satisfactory success ratio and avoids redundant message overhead. We use simulations and a real database called AudioScrobbler [CHECK END OF SENTENCE], which tracks users' listening habits, to evaluate the performance of SocioNet. The results show that SocioNet assists peers in locating content at peers with similar interests through short path lengths, and hence, achieves a higher success ratio (than nonsmall-world interest-based overlays and noninterest-based small-world overlays) while reducing message overhead significantly.

In wireless sensor networks (WSNs), due to scarce resources like energy, computational capability and storage space as well as rapid change in wireless link characteristics such as signal strength, interference, and multi-path propagation, how to provide a reliable data transmission in WSNs while prolonging the network lifetime as long as possible is an important and challenging issue. In general, there are several approaches, e.g., automatic repeat request, multi-path routing and source coding, used for providing reliable data transfer in WSNs. We note that the overhead and performance of such end-to-end single-path approaches are often dominated by some poor-quality links or nodes on the path. That is, these traditional approaches are not able to quickly and properly react in this multi-hop wireless environment. To cope with the above issues, in this work we would like to propose an efficient reliable data transfer scheme with network coding for WSNs. To guarantee expected reliability with appropriate overhead, we not only derive an analytic model to estimate the proper amount of redundancy, but also propose the cluster-based and distributed scheme to dynamically adjust the redundancy at each hop. Through simulations in ns2, our results show that, compared with existing reliable data transfer schemes, our approach can achieve the required reliability with significantly fewer transmissions.

Perceptual visual quality guarantee is of vital concern for the P2P streaming system. One of challenging issues for assuring overall video quality is to design an effective media chunk scheduler. To the best of our knowledge, there is still a lack of studies on integrating video characteristics into P2P media chunk scheduler. In this work, we attempt to integrate video characteristics, i.e. rate-distortion impact, into P2P media chunk scheduler. No more "rarest-first" strategy, "rate-distortion first" strategy (RD-first) is deployed so that the chunks with larger rate-distortion score can be spread more effectively. Predictably, such a strategy can be better against video quality degradation by losing some less important chunks under a bandwidth constrained network. Moreover, to adapt for dynamic networks, we propose a hybrid available bandwidth prober. The simulation results show that received video quality can be substantially improved up to 1.7 dB compared with existing P2P schedulers.

Underwater sensor networks (UWSNs) have attracted a lot of attention recently. Since data in UWSNs are transmitted by acoustic signals, the characteristics of a UWSN are different from those of a terrestrial sensor network. In other words, the high propagation delay of acoustic signals in UWSNs causes spatial-temporal uncertainty, and makes transmission scheduling in UWSNs a challenging problem. Hence, in this paper, we propose a spatial-temporal MAC scheduling protocol, called ST-MAC, which is designed to overcome spatial-temporal uncertainty based on TDMA-based MAC scheduling for energy saving and throughput improvement. We construct thespatial-temporalconflictgraph(ST-CG) to describe the conflict delays among transmission links explicitly, and model ST-MAC as a new vertex coloring problem of ST-CG. We then propose a novel heuristic, called thetraffic-basedone-steptrialapproach(TOTA), to solve the coloring problem. In order to obtain the optimal solution of the scheduling problem, we also derive a mixed integer linear programming (MILP) model. Finally, we present a comprehensive performance study via simulations. The results show that ST-MAC can perform better than existing MAC schemes (such as S-MAC, ECDiG, and T-Lohi) in terms of the network throughput and energy cost.

Since the effectiveness of the routing scheme directly affects resource (e.g. energy, bandwidth, and memory) usage in wireless sensor networks, research works on routing protocols have received considerable attention recently. In this paper, we propose a location-free point-to-point routing scheme, called virtual domain and coordinate routing (VDCR), in wireless sensor networks. VDCR applies the concepts of the virtual domain and the virtual coordinate to design its distributed assignment protocol and the location-free routing protocol. That is, the assignment protocol assigns each sensor node the virtual coordinate to represent the location in a network and the virtual domain to maintain the information about the existing shortest path that has been established during the coordinate assignment. Our VDCR is able to efficiently discover the routing path based on the assigned domains and coordinates. The simulations show that the routing path of VDCR is shorter than ABVCap by 62%-70%. Besides, VDCR require 63%-89% preprocess overhead of ABVCap. For realistic network topologies with obstacles, VDCR can improve the routing path by 64% and up to 96%, compared with GPSR that requires physical location information.

Due to the vehicle's movement and inherent characteristics of wireless channel, how to discover and retrieve the required contents has become a challenging issue in a hybrid network including vehicular ad hoc networks (VANETs) and the Internet. In this paper, we propose a social cluster-based P2P framework that estimates similarity and connection condition among peers to provide mobile peers efficient resource discovery and retrieval. The idea of our framework is to consider a mobile peer's preference and its connectivity such as the lifetime or the bandwidth for a wireless link. Specifically, there are two major components in our framework. First, a social cluster-based overlay structure and lifetime-aware flooding scheme enable mobile peers to discover resource through the Internet and VANETs. Second, a connectivity-aware retrieval scheme considers connection lifetime and bandwidth schedules and determines how to retrieve resource from available peers. The simulation results show that, compared with other existing P2P schemes, our social cluster-based P2P framework is able to quickly locate more available peers holding the required contents and achieve a higher retrieval ratio.

Current works on the joint routing and channel assignment (JRC) problem in wireless mesh networks (WMNs) assume that all links operate at the base rate. In other words, they do not consider the presence of multiple bit-rates. However, in multi-rate WMNs, the achievable throughput of a link operating at higher rates could be much less than its bit-rate when it has to contend with low-rate links. This is also known as the multirate sharing problem. To address the problem, we first present a numerical formulation for estimating the dynamic link capacity, which can indicate the degree of the multi-rate sharing problem. Next, we propose an optimization model called MR2C, which solves the JRC problem by considering the dynamic link capacity in order to maximize the throughput in multi-rate, multi-radio, multi-channel WMNs. We then reformulate the primal problem as a Lagrangian dual problem, and apply the subgradient method to approach the dual. Finally, since each subproblem in the dual problem is also a non-linear problem, we design a polynomialtime algorithm to solve it. Our experiment results demonstrate that the proposed capacity formulation can approximate the actual link throughput. We compare MR2C with the base-rate JRC model via simulations. The results show that MR2C achieves throughput gains of 3-4 times and recover from network failure efficiently.

Recently, rapid advance in wireless technologies and development of wireless communication services have enabled a growing demand on wireless spectrum. Many research works also show that traditional fixed spectrum assignment can not use spectrum efficiently. Cognitive Radio, which allows temporary usage for the unlicensed (CR) users, is proposed to cope with the problem. In this work, we propose an adaptive transmission protocol by limiting transmission probability (LTP) of the CR users to guarantee the performance of Primary User under an 802.11 environment. Specifically, our LTP scheme uses 2 analytic models to figure out the relationship between current load of primary users and the optimal transmission parameter setting that can guarantee the throughput of primary users.

In this paper, we address the challenges of content transfer in opportunistic networks, and propose techniques to better facilitate data dissemination based on the characteristics of the content. To investigate this problem from its origins, we propose three message scheduling algorithms: Sequential Forwarding (SF), Full Interleaving (FI), and Block-based Interleaving (BI). Each algorithm is embedded in a specially tailored data dissemination technique to evaluate the benefits of applying it to different types of content and data dissemination methods. Three types of content (file, video and web) are considered and evaluated, and the dissemination methods considered are Layered Multiple Description Coding (LMDC) based and file-based. Using simulations as well as both synthetic and realistic network scenarios, we evaluate the proposed schemes in terms of latency and user perceived quality, and demonstrate how the schemes can achieve much better latency performance for file transfers. Furthermore, we show that using LMDC-based techniques leads to higher user perceived quality, since the end user is allowed to "preview" video file or web content, even before the data has been completely transferred. The effectiveness and robustness of our message scheduling algorithms and their corresponding content dissemination techniques make them ideal solutions that can go a long way toward effective data dissemination in opportunistic networks.

For low-delay video transmission, we introduce aq-domain characteristic-based bit-rate model. Specifically, three characteristics are efficiently extracted from the quantized DCT spectra to construct the bit-rate model. Extensive experimental results show that our rate model can provide more accuracy with lower complexity than existing models.

We propose a simple and practical solution, called the virtual interface-based approach (VIP), for seamless vertical handover in single-subnet networks. VIP combines a virtual interface and ARP update messages to achieve vertical handovers, and can run stand-alone without modification of existing network protocols or installation of additional servers. Therefore, it is ready for immediate real-world deployment. Using testbed experiments, we evaluate VIP by observing TCP (FTP downloading) behavior in various vertical handover scenarios. The results show that the proposed approach can successfully maintain an application's connectivity in all test cases. Furthermore, the results show that handover from a low capacity link to a high capacity link does not cause service latency; however, during handover from a high capacity link to a lower capacity link, non-negligible latency can not be alleviated unless early handover notification is provided.

In multi-channel wireless mesh networks (WMNs), the routing and channel assignment can interdependently determine network capacity. However, joint routing and channel assignment (JRC) is a challenging problem since it must satisfy theRadioConstraint,theChannelConstraint,andtheCo-channelInterferenceConstraint. Recently, many heuristic or suboptimization algorithms have been proposed to address the JRC problem. The aim of this paper is to preform a comprehensive performance study to fairly compare the different approaches. To achieve this goal, a proper benchmark, a mixed integer linear programming (MILP) model that can optimally configure the routing and channel assignment to get maximum achievable network capacity, is provided in this work as well. The proposed MILP model can help system designers quantitatively evaluate existing heuristic methods and make a better decision by considering the tradeoff between the characteristics of various schemes according to the system requirements.

How to perform rate control on emerging H.264/AVC, which includes the non-normative but widely accepted rate-distortion optimization (RDO) process, i.e., the rate-constrained motion estimation and mode decision, has become a challenging and difficult research issue. Usually, rate controller is useful at given residual signals to determine quantization parameter (QP). However, to perform RDO, a QP should first be predetermined. This leads to a classical chicken and egg dilemma. Therefore, how to understand and predict the coding behavior after RDO with different QPs is the core problem of the H.264/AVC rate controller. In this work, we attempt to adopt coding behaviors of customized residues that are extracted from part of RDO to forward estimate the coding behavior of RDO with different QPs. Experiments show that the proposed rate-quantization (R-Q) model is able to provide a more accurate estimation of the actual bit rate than existing MAD-based model in JM 10.2.

In this paper, we present a two-layer rate control framework for low delay video transmission based on a characteristic-based rate-quantization (R-Q) model. Specifically, with the frame-layer characteristic-based R-Q model, we are able to figure out the most appropriate quantization parameter (QP) to maintain targeted end-to-end delay by jointly considering the current buffer status, network bandwidth and the variation of bit rate of the input video source. Next, in macroblock-layer (MB), a greed-based rate-distortion (R-D) refinement of MBs approach is proposed to efficiently utilize the remaining available bit rate in order to enhance visual quality as much as possible. The experiments show that our two-layer characteristic-based rate control framework can quickly smooth out the impact of varying bit rate of video source on the buffer, meet targeted buffer delay time well, and substantially enhance the quality of the perceived video with lower computational complexity.

Recently, peer-to-peer systems have become one of the most popular distributed applications. Many previous works have investigated identifier-based indexing systems that support a query-by-identifier service. However, clients usually have only partial information about an object, and prefer to query by keywords. In this paper, we propose a small-world-based keyword search system (SW-KSS) that provides keyword search and similarity search services simultaneously. The proposed SW-KSS applies the concept of the "small world theory" to the construction of an indexing structure. Such structures mirror the way humans keep track of their friends and acquaintances; hence, they can cluster peers who share common interests. The method enables a peer to And objects of interest from similar neighboring peers efficiently. We evaluate the performance of SW-KSS via simulations. The results show that SW-KSS can achieve both scalability and partial-match look-up capability.

In this paper, we analyze the limitation of ρ-domain based rate-quantization (R-Q) model. We find out that a characteristic-based R-Q model can be derived from ρ-domain to q-domain. Experimental data show that such a characteristic-based R-Q model can provide a more accurate estimation of the actual bitrate than existing models for both frame-level and macroblock(MB)-level. In addition, a simple analysis of computational complexity of our quantization-free characteristics extraction framework shows that our model is faster than existing variance and ρ-domain based R-Q models.

Multi-channel wireless mesh networks (WMNs) aim to perform ubiquitous wireless broadband network access. In WMNs, much attention has been paid to the problem of resource allocation, i.e., how to utilize multiple orthogonal channels and multiple communication radios to enhance the aggregate throughput efficiently. Routing and resource allocation correlatively determine the performance of WMNs. Thus, this paper proposes a route-aware resource allocation algorithm to distribute total traffic load over diverse radios and channels based on route information. The simulation results show that route-aware resource allocation can balance the workload among all available resources (radios and channels) and achieve the higher aggregate throughput and fairness.

Since interests in multimedia services have been growing with the proliferation of wireless networks, there is a need to provide an efficient loss-differential scheme for the wired-cum-wireless networks. This is important because with the aid of the differential scheme, the congestion control protocol can tell the packet loss due to the error-prone wireless link or the network congestion. Then, the congestion control protocol will avoid reducing the sending rate blindly in the present of the packet loss. In this work, we propose an end-to-end loss-differential approach, called Trend-and-Loss-Density-based (TD) scheme. Since the congestion losses are highly co-related to each other, the TD scheme considers (a) the trend to indicate where the packet loss happens, and (b) the loss density to examine how often the packet loss occurs. Specifically, we use the autocorrelation function to measure the dense degree of a packet loss in a given sequence. Moreover, the threshold of each trend in the TD scheme is decided according to the characteristics of the wireless channel so it is easily adapted to different wireless network situations. We evaluate the TD scheme via extensive simulations in ns2. The results show that compared with other differential schemes, the TD scheme can substantially improve wireless and overall misclassification rates while maintaining a comparable throughput in all experiments.

Finding the routing path for disseminating data to mobile sinks in the wireless sensor networks is a challenging problem due to the random mobility of sinks and the limited resources of sensors, such as energy, storage capacity, and computing capability. Although flooding the location information of mobile sinks seems to be a naive method to find the path between the data source and the mobile sink, it drains much power of the sensor nodes. Thus, we propose a Hierarchical Cluster-based Data Dissemination scheme, named HCDD, to disseminate data to the mobile sink with light control overhead. In HCDD, the sensor nodes are self-organized to find the route without the knowledge of node's location information. That is, unlike other works, the HCDD can operate without any expensive and power-consuming GPS device used for estimating the location information. The simulation results show that our HCDD scheme can greatly alleviate the control overhead, and, at the same time, achieve longer network lifetime and comparable number of received data with previous works, such as the TTDD-like methods.

Recently, with the rapid development of wireless technologies and the attractive infrastructure-free nature of MANET, there is a growing demand for diverse multimedia applications that require QoS provision in an ad hoc network. In this paper, we propose a topology-control-based QoS routing (TLQR) protocol, which is a distributed scheme to intelligently establish routes with guaranteed end-to-end bandwidth. That is, we provide a simple residual-hop-count procedure to determine whether a node is capable to accept the new connection request or not. In addition, to enhance the network utilization we incorporate a efficient power adjustment into the route discovery procedure. The simulation results show that the TLQR routing scheme is able to (a) figure out a better route not going through the congestion area in the network, and (b) significantly improve the network performance by adaptively adjusting its transmission power, i.e., achieving better spacial reuse of wireless channel. Thus, we believe that it is a promising way to take the topology control into account while designing a QoS routing protocol in an ad hoc network

Sensor networks are large-scale distributed sensing networks comprised of many small sensing devices equipped with memory, processors, and short-range wireless communication radio. Instead of broadcast-based routing protocols, in this paper we propose a novel energy-efficient routing protocol, which is called straight line routing algorithm (SLR), for wireless sensor networks. To achieve the routing task without broadcasting, the source host constructs the event path and the sink host constructs the query path respectively. That is, the routing path is found as the query path and the event path first intersect. Moreover, the SLR is able to build both the query path and the event path without any help of the geographic information. We evaluate the performance of straight line routing and rumor routing protocols through extensive simulations. The simulation results indicate that compared with rumor routing, the SLR can save more energy consumption, provide better path quality, and improve the successful ratio of routing as well.

Adequate bandwidth allocations and strict delay requirements are critical for real time applications. Packet scheduling algorithms like class based queue (CBQ), nested deficit round robin (nested-DRR) are designed to ensure the bandwidth reservation function. However, they might cause unsteady packet latencies and introduce extra application handling overhead, such as allocating a large buffer for playing the media stream. High and unstable latency of packets might jeopardize the corresponding quality of service since real-time applications prefer low playback latency. Existing scheduling algorithms which keep latency of packets stable require knowing the details of individual flows. GPS (general processor sharing)-like algorithms does not consider the real behavior of a stream. A real stream is not perfectly smooth after forwarding by the routers. GPS-like algorithms introduces extra delay on the stream which is not perfectly smooth. This thesis presents an algorithm which provides low latency and efficient packet scheduling service for streaming applications called LLEPS.

We consider the problem of collecting a large amount of data from several different hosts to a single destination in a wide-area network. This problem is important since improvements in data collection times in many applications such as wide-area upload applications, high-performance computing applications, and data mining applications are crucial to performance of those applications. Often, due to congestion conditions, the paths chosen by the network may have poor throughput. By choosing an alternate route at the application level, we may be able to obtain substantially faster completion time. This data collection problem is a nontrivial one because the issue is not only to avoid congested link(s), but to devise a coordinated transfer schedule which would afford maximum possible utilization of available network resources. Our approach for computing coordinated data collection schedules makes no assumptions about knowledge of the topology of the network or the capacity available on individual links of the network. This approach provides significant performance improvements under various degrees and types of network congestions. To show this, we give a comprehensive comparison study of the various approaches to the data collection problem which considers performance, robustness, and adaptation characteristics of the different data collection methods. The adaptation to network conditions characteristics are important as the above applications are long lasting, i.e., it is likely changes in network conditions will occur during the data transfer process. In general, our approach can be used for solving arbitrary data movement problems over the Internet. We use the Bistro platform to illustrate one application of our techniques.

In this paper we consider the problem of collecting a large amount of data from several different hosts to a single destination in a wide-area network. Often, due to congestion conditions, the paths chosen by the network may have poor throughput. By choosing an alternate route at the application level, we may be able to obtain substantially faster completion time. This data collection problem is a nontrivial one because the issue is not only to avoid congested link(s), but to devise a coordinated transfer schedule which would afford maximum possible utilization of available network resources. In this paper we present an approach for computing coordinated data collection schedules, which can result in significant performance improvements. We make no assumptions about knowledge of the topology of the network or the capacity available on individual links of the network, i.e., we only use end-to-end information. Finally, we also study the shortcomings of this approach in terms of the gap between the theoretical formulation and the resulting data transfers in wide-area networks. In general, our approach can be used for solving arbitrary data movement problems over the Internet. We use the Bistro platform to illustrate one application of our techniques.

Recent technological advances in digital signal processing, data compression techniques, and high-speed communication networks have made Video-on-Demand (VOD) servers feasible. A challenging task in such systems is servicing multiple clients simultaneously while satisfying real-time requirements of continuous delivery of objects at specified rates. To accomplish these tasks and realize economies of scale associated with servicing a large user population, a VOD server requires a large disk subsystem. Although a single disk is fairly reliable, a large disk farm can have an unacceptably high probability of disk failure. Furthermore, due to real-time constraints, the reliability requirements of VOD systems are even more stringent than those of traditional information systems. Traditional RAID solutions are inadequate due to poor resource usage. Thus, in this paper, we present alternative schemes which provide a high degree of reliability at low disk storage, bandwidth, and memory costs for on-demand multimedia servers. Moreover, we discuss some of the main issues and trade-offs associated with providing fault tolerance in multidisk VOD systems. We would like to impress upon the reader that one of the main points of this paper is the exposition of trade-offs and issues associated with designing fault-tolerant VOD servers. It is not the case that one fault tolerance scheme is absolutely better than another, but rather that one must understand the trade-offs as well as one's system constraints and then choose a fault tolerance scheme accordingly.

Multimedia applications place high demands for QoS, performance, and reliability on storage servers and communication networks. These, often stringent requirements, make design of cost-effective and scalable continuous media (CM) servers difficult. In particular, the choice of data placement techniques can have a significant effect on the scalability of the CM server and its ability to utilize resources efficiently. In the recent past, a great deal of work has focused on "wide" data striping. Another approach to dealing with load imbalance problems is replication. The appropriate compromise between the degree of striping and the degree of replication is key do the design of scalable CM servers. Thus, the main focus of the paper is a study of scalability characteristics of CM servers as a function of tradeoffs between striping and replication.

The stringent requirements of multimedia applications make design of cost-effective and scalable systems difficult; thus, efficient adaptive and dynamic resource management techniques can be of great help in improving performance and scalability characteristics of such systems. In this paper we focus on threshold-based policies for dynamic resource management in the context of continuous media (CM) servers; we do this without any knowledge of data access patterns and with provisions for full use of VCR functionality. We propose a mathematical model of user behavior and show through a performance study, that not only does the use of this model in conjunction with dynamic resource management policies improves the system's performance but that it also facilitates significantly reduced sensitivity to changes in: (a) system architecture, (b) workload characteristics, (c) skewness of data access patterns, (d) frequency of changes in data access patterns, and (e) choice of threshold values. We believe that not only is this a desirable property for a CM server, in general, but that furthermore, it suggests the usefulness of these techniques across a wide range of continuous media applications.

No abstract available.