Email, as a primary communication service, plays a crucial role in communication, making it one of the most common targets of cyberattacks. Masquerade attacks, which involve impersonating legitimate users to obtain authentication credentials and gain unauthorized access to services, are particularly common. Since masquerade attacks use legitimate logins, their dwell-time behavior resembles normal users, making detection challenging. Further complicating the issue are the diverse email usage patterns, making the identification of detection results time-consuming and labor-intensive. To slove those problems, we propose a rule-based, self-training, semi-supervised model for masquerade attack detection. It analyzes four types of email logs and classifies usage scenarios. It uses an Auto-Encoder for semisupervised detection with limited labeled data, refining results through iterative self-training with coarse-grained rules. The framework is validated on email data from 6,736 users over three months, identifying 7 anomalous accounts and 12 anomalous IP addresses. Compared to the company's Security Operations Center security tools, the proposed framework detected 3 more anomalous accounts and 7 more anomalous IP while reducing suspicious accounts by 3.72 times and suspicious IP by 3.54 times.

Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this paper, we introduce “Abductive Past Action Inference”, a novel research task aimed at identifying the past actions performed by individuals within homes to reach specific states captured in a single image, using abductive inference. The research explores three key abductive inference problems: past action set prediction, past action sequence prediction, and abductive past action verification. We introduce several models tailored for abductive past action inference, including a relational graph neural network, a relational bilinear pooling model, and a relational transformer model. Notably, the newly proposed object-relational bilinear graph encoder-decoder (BiGED) model emerges as the most effective among all methods evaluated, demonstrating good proficiency in handling the intricacies of the Action Genome dataset. The contributions of this research significantly advance the ability of deep learning models to reason about current scene evidence and make highly plausible inferences about past human actions. This advancement enables a deeper understanding of events and behaviors, which can enhance decision-making and improve system capabilities across various real-world applications such as Human-Robot Interaction and Elderly Care and Health Monitoring. Code and data available at https://github.com/LUNAProject22/AAR

ABSTRACT Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real‐world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high‐dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer‐based Variational AutoEncoder (T‐VAE) for anomaly perception in multivariate time series data. The T‐VAE consists of two sub‐networks, the Representation Network and the Memory Network, and achieves end‐to‐end jointly optimisation. The Representation Network leverages self‐attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory Network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high‐volatility and noisy data to the distribution of the normal data. We evaluate T‐VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses.

Multi-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: (1) multiple views within a single client; (2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives.

The resolution of proof-of-work problem in blockchain requires significant amount of resources, while the lack of computing power on mobile devices limits the development of blockchain in mobile applications. To mitigate this issue, the combination of blockchain and mobile edge computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system that includes one single edge service provider (ESP), multiple types of miners and edge nodes. Each miner submits offloading request to ESP. In response, ESP designs contract to incentivize various types of edge nodes to contribute resources and offer computational services to the miners. This problem is a joint optimization problem of offloading decisions and contract design. Due to the time-variability of network environment, the randomness of miners’ task demands, and the asymmetric information between the ESP and edge nodes, solving this problem is challenging. We propose a deep reinforcement learning contract mechanism (DRLCM) for incentive-based computation offloading strategies, which divides the original problem into two sub-problems: computation offloading and contract design. Initially, the deep Q-network (DQN) algorithm is used to update the offloading decisions based on the evolving task demands and network conditions. Secondly, contract is designed to motivate edge nodes to participate in resource sharing. The problem is simplified by analyzing the necessary and sufficient conditions of feasible contract, and the Lagrange multiplier method is used to approximate the optimal contract. Simulation experiments demonstrate the effectiveness of the DRLCM algorithm, which shows better convergence and performance compared to traditional DQN, Double-DQN algorithms and Dueling-DQN.

The smart manufacturing revolution, driven by advanced technologies such as Artificial Intelligence and Internet of Things, is rapidly reshaping the world economy. One critical component of this revolution is industrial inspection and image analysis for anomalies, which are now being boosted by deep learning techniques capable of extracting latent features for high-quality defect detection. However, most existing unsupervised approaches combining deep models and outlier detection models pay little attention to the critical issue of threshold and relevant estimation, which is crucial for practical deployments. In this work, we propose a threshold estimation-assisted patch-wise approach that involves a core set selection of representative nominal patch features, anomaly scoring through comparing inputs and the core set, and a permutation-assisted threshold estimation. Our proposed model is technically practical for industrial deployment and addresses the cold-start problem with reduced labor for image annotation. To enhance the selection and scoring process, we also introduce novel position encoding and neighbor-assisted schemes. Our approach outperforms popular state-of-the-art baselines in extensive experiments involving images from different industry domains, demonstrating its practical effectiveness.

• We propose a hybrid model, ECR-BERT, for explainable sentiment analysis. • We infer emotion-cognitive knowledge based on the OCC model. • We propose a self-adaptive fusion algorithm to mitigate the knowledge noise problem. • We adopt knowledge-enabled feature representation to efficiently utilize knowledge. • Evaluation on four real-world Weibo datasets shows the efficacy of our method. Sentiment analysis of online public opinions on emergencies (OPOEs) requires accurate and explainable results to facilitate a better understanding of public sentiment and effective crisis management, but it is challenging due to the complexity and diversity of emotions contained in OPOEs. In this paper, we propose an Emotion-Cognitive Reasoning integrated BERT (ECR-BERT) for sentiment analysis of OPOEs. ECR-BERT combines an emotion model and deep learning to provide reliable auxiliary knowledge to improve BERT. Specifically, we use the emotion model proposed by Ortony, Clore, and Collins (OCC) to build emotion-cognitive rules and perform emotion-cognitive reasoning to discover emotion-cognitive knowledge. To mitigate the impact of knowledge noise, we propose a novel self-adaptive fusion algorithm that provides a selection mechanism for the incorporation of knowledge. In addition, we utilize knowledge-enabled feature representation to efficiently exploit inferred knowledge. Our evaluation on four real-world OPOE datasets shows that ECR-BERT significantly outperforms other BERT-based models, achieving state-of-the-art results with an absolute average accuracy improvement of 0.82%, 1.74%, 0.98%, and 1.37% over BERT, respectively. In addition, ECR-BERT provides a detailed explanation of how sentiment polarity is derived from fine-grained emotion categories. The ablation study demonstrates the effectiveness of each technique. In conclusion, ECR-BERT is an excellent choice for sentiment analysis of OPOEs, providing accurate and explainable results for crisis management.

Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this paper, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. Firstly, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Secondly, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.

The meteorological environment has a profound impact on global health (e.g., air quality), science and technology (e.g., rocket launches), and economic development (e.g., poverty reduction) etc. Meteorological prediction presents numerous challenges to both academia and industry due to its multifaceted nature which encompasses real-time observations and complex modeling. Recent research adopt graph convolutional recurrent network and establish coordinate information to obtain local spatial-temporal pattern. However, the model only utilizes the local spatial-temporal information and fail to fully consider the dynamic meteorological situation. To address the above limitations, we propose a Dynamic Spatial-Temporal Graph Neural Network (DSTGNN) to learn global-local meteorological features. Specifically, we divide the global spatial-temporal information along the timeline to obtain local spatial-temporal information. For the global aspect, we design a random throwedge module during the neighborhood propagation process in graph neural network (GNN) to extract the features and adapt to the dynamic situation. We also establish convolution operation module to learn the features. Next, we perform information fusion on the two modules to capture sufficient features. In addition, we employ graph ordinary differential equation (ODE) network and utilize the coordinate information to obtain the long-term features and coordinate relationships. In the local aspect, we first construct a GNN to conduct graph embedding. Then, we integrate another GNN into a gated recurrent unit (GRU) and also use the coordinate information to explore the features and coordinate relationships. Finally, we combine the global and local features via a global-local features learning layer for meteorological prediction. Experimental results on the four real-world meteorological datasets show that DSTGNN outperforms the baseline models.

The resource allocation problem in Multi-access Edge Computing (MEC) has been widely studied to maximize its operation efficiency under limited resource constraint. However, the existing literatures overlooked the setup cost and the associated dynamic operations. In this work, we consider server configuration and overload in the multi-server scenario where servers are switched on/off depending on the network environment. A novel pricing mechanism maximizing the utility of base station (BS) monitoring multiple servers is proposed, which jointly optimizes the setup cost and server load. We aim to maximize the BS utility under one-day task requests, and divide the time into off-peak and peak periods based on task requests. In the off-peak period, we flexibly switch on/off servers for BS to reduce setup costs. In the peak period, to avoid overloading, we introduce crowdsourcing where servers as agents purchase idle resources from private users (PUs) for mobile users (MUs) and minimize MUs’ cost by a contract-based knapsack algorithm. Lastly, a pricing mechanism is proposed to solve the BS utility maximization problem with an exploratory Upper Confidence Bound (UCB)-based algorithm adjusting server prices dynamically. Simulation results show that the proposed algorithm is superior to others in minimizing MUs cost and maximizing BS utility.

In this paper, we address the challenging problem of 3D toonification, which involves transferring the style of an artistic domain onto a target 3D face with stylized geometry and texture. Although fine-tuning a pre-trained 3D GAN on the artistic domain can produce reasonable performance, this strategy has limitations in the 3D domain. In particular, fine-tuning can deteriorate the original GAN latent space, which affects subsequent semantic editing, and requires independent optimization and storage for each new style, limiting flexibility and efficient deployment. To overcome these challenges, we propose DeformToon3d, an effective toonification framework tailored for hierarchical 3D GAN. Our approach decomposes 3D toonification into subproblems of geometry and texture stylization to better preserve the original latent space. Specifically, we devise a novel StyleField that predicts conditional 3D deformation to align a real-space NeRF to the style space for geometry stylization. Thanks to the StyleField formulation, which already handles geometry stylization well, texture stylization can be achieved conveniently via adaptive style mixing that injects information of the artistic domain into the decoder of the pre-trained 3D GAN. Due to the unique design, our method enables flexible style degree control and shape-texture-specific style swap. Furthermore, we achieve efficient training without any real-world 2D-3D training pairs but proxy samples synthesized from off-the-shelf 2D toonification models. Code is released at https://github.com/junzhezhang/DeformToon3D.

How resources are deployed to secure critical targets in networks can be modelled by Network Security Games (NSGs). While recent advances in deep learning (DL) provide a powerful approach to dealing with large-scale NSGs, DL methods such as NSG-NFSP suffer from the problem of data inefficiency. Furthermore, due to centralized control, they cannot scale to scenarios with a large number of resources. In this paper, we propose a novel DL-based method, NSGZero, to learn a non-exploitable policy in NSGs. NSGZero improves data efficiency by performing planning with neural Monte Carlo Tree Search (MCTS). Our main contributions are threefold. First, we design deep neural networks (DNNs) to perform neural MCTS in NSGs. Second, we enable neural MCTS with decentralized control, making NSGZero applicable to NSGs with many resources. Third, we provide an efficient learning paradigm, to achieve joint training of the DNNs in NSGZero. Compared to state-of-the-art algorithms, our method achieves significantly better data efficiency and scalability.

In this paper, we consider a mobile edge computing (MEC) system with multiple unmanned aerial vehicles (UAVs) and stochastic energy harvesting. The UAVs' mobility can help data offloading over a larger geographical area containing multi- hotspots (HSs). If HSs have offloading requests, the dispatch agent (DA) can recruit different types of UAVs to fly close to HSs and help computation. We aim to maximize the long-term utility of all HSs, subject to the stability of energy queue. The proposed problem is a joint optimization problem of offloading strategy and contract design in a dynamic setting over time. We design a deep reinforcement learning based contract incentive (DRLCI) strategy that solves the joint optimization problem in two steps. Firstly, we use an improved deep Q-network (DQN) algorithm to obtain the offloading decision. Secondly, to motivate UAVs to participate in resources sharing, a contract has been designed for asymmetric information scenarios, and Lagrangian multiplier method has been utilized to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy. It can achieve a very close-to the performance obtained by complete information scenario.

In the information age, a secure and stable network environment is essential and hence intrusion detection is critical for any networks. In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOM-DAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection. The deep autoencoding Gaussian mixture model comprises a compression network and an estimation network which is able to perform unsupervised joint training. However, the code generated by the autoencoder is inept at preserving the topology of the input space, which is rooted in the bottleneck of the adopted deep structure. A self-organizing map has been introduced to construct SOM-DAGMM for addressing this issue. The superiority of the proposed SOM-DAGMM is empirically demonstrated with extensive experiments conducted upon two datasets. Experimental results show that SOM-DAGMM outperforms state-of-the-art DAGMM on all tests, and achieves up to 15.58% improvement in F1 score and with better stability.

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and learn in a fully super-vised manner. Despite their impressive performances on in-domain data, when generalizing to partial shapes in other forms or real-world partial scans, they often obtain unsatisfactory results due to domain gaps. In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time. ShapeInversion uses a GAN pre-trained on complete shapes by searching for a latent code that gives a complete shape that best reconstructs the given partial input. In this way, ShapeInversion no longer needs paired training data, and is capable of incorporating the rich prior captured in a well-trained generative model. On the ShapeNet bench-mark, the proposed ShapeInversion outperforms the SOTA unsupervised method, and is comparable with supervised methods that are learned using paired data. It also demonstrates remarkable generalization ability, giving robust results for real-world scans and partial inputs of various forms and incompleteness levels. Importantly, ShapeInversion naturally enables a series of additional abilities thanks to the involvement of a pre-trained GAN, such as producing multiple valid complete shapes for an ambiguous partial input, as well as shape manipulation and interpolation.

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as Network Security Games (NSGs). Unfortunately, existing approaches, including the deep learning-based approaches, are inefficient to solve large-scale extensive-form NSGs. In this paper, we propose a novel learning paradigm, NSG-NFSP, to solve large-scale extensive-form NSGs based on Neural Fictitious Self-Play (NFSP). Our main contributions include: i) reforming the best response (BR) policy network in NFSP to be a mapping from action-state pair to action-value, to make the calculation of BR possible in NSGs; ii) converting the average policy network of an NFSP agent into a metric-based classifier, helping the agent to assign distributions only on legal actions rather than all actions; iii) enabling NFSP with high-level actions, which can benefit training efficiency and stability in NSGs; and iv) leveraging information contained in graphs of NSGs by learning efficient graph node embeddings. Our algorithm significantly outperforms state-of-the-art algorithms in both scalability and solution quality.

Stock index prediction is a complex problem as stock indices movement is influenced by many factors and events possibly affecting the economy. With the increasingly efficient algorithms and techniques in machine learning, there is a renewed interest in the use of machine learning to predict the flow of the stock index. In this paper, technical features of the New York Stock Exchange Composite (NYA) are derived. Content features from carefully curated Twitter accounts are also gathered so as to analyze and explore machine learning techniques to predict the movement of the NYA. Probabilistic model for sentiment features are derived and a simple recurrent neural network (Elman networks) with gated recurrent units was implemented for the prediction model. The results of the study showed that probabilistic sentiment analysis of twitter news applied to a simple recurrent neural network significantly improves the prediction performance.

Here, the authors study the novel problem of optimising access point (AP) association by maximising the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimisation. They resort to the Markov Chain approximation technique to design a distributed algorithm. They first approximate their optimal objective via Log‐Sum‐Exp function. Thereafter, they construct a special class of Markov Chain with steady‐state distribution specify to their problem to yield a distributed solution. Furthermore, they extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Their proposed algorithm has provable performance, achieving an approximation gap of . It is simple and can be implemented in a distributed manner. Their extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close‐to‐optimal performance with a guaranteed loss bound.

The heterogeneous network structure is a promising paradigm to improve the quality of service across the entire network. Nevertheless, such a structure is challenging due to the presence of multiple‐tier secondary users (SUs). In this study, the authors investigated the effect of spectrum allocation in heterogeneous cognitive radio networks with a primary network and two‐tier secondary networks, and proposed a two‐tier spectrum trading strategy which includes two trading processes. In Process One, they model the spectrum trading as a monopoly market, where the primary spectrum owner (PO) acts as the monopolist and the first‐tier secondary users (FSUs) act as the buyers. They design an optimal quality‐price contract to maximise the utility of PO, and the FSUs will choose the spectrum with appropriate quality and price to enhance their satisfaction. In Process Two, spectrum trading is modelled as a multi‐seller, multi‐buyer market. The dynamic behaviour of second‐tier SUs is studied using the theory of evolution game, while the competition among FSUs is analysed via a non‐cooperative game where the Nash equilibrium is considered as the solution. The existences of the optimal contract, evolutionary equilibrium and Nash equilibrium are demonstrated in the performance evaluation.

Although many public areas have installed CCTV to help monitor the traffic conditions, manually inspecting these videos to recognize unauthorized parking behaviors is extremely tedious and inefficient. In this paper, we propose a framework for automatic detection of illegally parked vehicle. The framework comprises two major components, namely object detection and movement tracking. To be more specific, we adopt one of the most prevalent object detection algorithm YOLO (v3) to detect vehicles and template matching methods using normalized cross correlation for movement tracking. Experiments show that the proposed method can achieve a very high accuracy and is robust to different camera angles, weather conditions and illuminations of the video.

Wearable Ambulatory Monitor (WAM) is a portable electronic device that monitor the body's function. WAM is used daily and has high potential for home rehabilitation. However, it is limited by its high cost. The arrival of smartphones provides an alternative for motion tracking as they have a number of motion sensors. However, most smartphone applications only use them to detect general movement and not precise motion. A Complementary Feedback Filter (CFF) is proposed and developed to fuse readings from the smartphone's noisy motion sensors in order to get accurate orientation of body segments. The experimental results have shown that movement orientation accuracy obtained from applying CFF on smartphone sensors is comparable to XSENS Awinda, a commercial motion tracking system. The combination of the proposed CFF and the smartphone's motion sensors are then applied to RehabPartner, a motion tracking application for the elderly to do rehabilitation exercises at home.

In 2008, the Federal Communications Commission issued a ruling permitting the unlicensed usage of TV white spaces (TVWS), i.e. locally vacant TV channels. Due to its low‐frequency range (50–698 MHz), the TV spectrum has much better propagation characteristic and higher‐spectral efficiency, resulting in a wide range of potentially important applications. However, unlike typical cellular and industrial scientific medical bands, TVWS are subjected to high‐spatial variation, temporal variation, and fragmentation, resulting in new challenges in TVWS identification and in implementing a wireless network in this band. Identification and network design are the two key issues required to be addressed while investigating TVWS. These two problems have been widely discussed in several existing literature. Applications in TVWS are also an important topic, which has not been adequately explored. This study provides an up‐to‐date survey of TVWS and its applications in future wireless networks and communication. Various problems and challenges associated with each use case as well as the possible enabling methods to address these challenges are also presented.

Image outlier detection has been an important research issue for many computer vision tasks . However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder . Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.

Availability and all-in-one functionality of smartphones have become a multipurpose personal tool to improve our daily life. Recent advancements in hardware and accessibility of smartphones have spawn huge potential for assistive healthcare, in particular telerehabilitation. However, using smartphone sensors face certain challenges, in particular, accurate orientation estimation, which is usually less of a problem in specialized motion tracking sensor devices. Drift is one of the challenges. We first propose a simple feedback loop complementary filter (CFF) to reduce the error caused by the integration of the gyroscope's data in the orientation estimation. Next, we propose a new and better orientation estimation algorithm which combines quaternion-based kalman filter with corrector estimates using gradient descent (KFGD). We then evaluate CFF's and KFGD's performance on two early-stage rehabilitation exercises. The results show that CFF is capable of fast motion tracking and confirm that the feedback loop can correct the error caused by the integration of gyroscope data. The KFGD orientation estimation is comparable to XSENS Awinda and has shown itself to be stable than and outperforms CFF. KFGD also outperforms the prominent Madgwick algorithm using mobile data. Thus, KFGD is suitable for low-cost motion sensors or mobile inertial sensors, especially during early recovery stage of sport injuries and exercise for the elderly.

We consider a vehicular content replication system which makes use of the deployed Access Points (APs) to maximize the vehicular download progress of delay-tolerant contents through replication in the APs' local storage. The transient connection period between the vehicle and the AP makes it difficult for the vehicle to download the entire file requested and thus the content retrieval is usually across several APs. We propose an efficient and distributed replication algorithm explicitly taking into account the content popularity, vehicle-AP contact pattern, and content availability among correlated APs. Simulation based on real vehicular trace proves the effectiveness of the proposed replication system. The performance in terms of download rate and completion ratio has at least 15% to 20% improvement against the algorithms under comparison.

One important issue associated with spectrum management in heterogeneous cognitive radio network is: how to appropriately allocate the spectrum to the secondary senderdestination (S-D) pair for sensing and utilization. In this work, the authors investigate the spectrum allocation problem under a more practical scenario, taking the heterogeneous characteristics of both secondary S-D and PU channels into consideration. With the objective to maximize the achievable throughput for secondary S-D, we formulate the spectrum allocation problem as a linear integer optimization problem under spectrum availability constraint, spectrum span constraint and interference free constraint. This problem is NP-complete, and a recent result in theoretical computer science called randomized rounding algorithm with polynomial computational complexity is developed to find the ρ-approximation solution. Evaluation results show that our proposed algorithm can achieve a close-to-optimal solution while keeping the complexity low.

Delay Tolerant Network (DTN) is developed to cope with intermittent connectivity and long delay in wireless networks. Due to the limited connectivity, DTN is vulnerable to blackhole and greyhole attacks in which malicious nodes intentionally drop all or part of the received messages. Although existing proposals could accurately detect the attack launched by individuals, they fail to tackle the case that malicious nodes cooperate with each other to cheat the defense system. In this paper, we suggest a scheme called Statistical-based Detection of Blackhole and Greyhole attackers (SDBG) to address both individual and collusion attacks. Nodes are required to exchange their encounter record histories, based on which other nodes can evaluate their forwarding behaviors. To detect the individual misbehavior, we define forwarding ratio metrics that can distinguish the behavious of attackers from normal nodes. Malicious nodes might avoid being detected by colluding to manipulate their forwarding ratio metrics. To continuously drop messages and promote the metrics at the same time, attackers need to create fake encounter records frequently and with high forged numbers of sent messages. We exploit the abnormal pattern of appearance frequency and number of sent messages in fake encounters to design a robust algorithm to detect colluding attackers. Extensive simulation shows that our solution can work with various dropping probabilities and different number of attackers per collusion at high accuracy and low false positive.

Delay Tolerant Network (DTN) is developed to cope with intermittent connectivity and long delay in wireless networks. Due to the limited connectivity, DTN is vulnerable to blackhole and greyhole attacks in which malicious nodes drop all or part of the received packets intentionally. Although existing proposals could detect the attack launched by individuals, they fail to tackle malicious nodes cooperating to cheat the defense system. In this paper, we suggest a scheme to address both individual and collusion attacks. Nodes are required to exchange records of previous encounters and evaluate others based on their messages forwarding ratios. Malicious nodes might avoid being detected by colluding to hide misbehaving forwarding ratio metrics. To persistently drop packets and promote the metrics at the same time, attackers need to create forged encounter records at high frequency and with high number of sent messages. This leads to abnormal patterns of fake encounters in contrast with authentic ones and provides a symptom for collusion detection. Extensive simulation shows that our solution can work with various dropping probabilities and different number of attackers per collusion at high accuracy and low false positive.

Spectrum sensing and access have been widely investigated in cognitive radio network for the secondary users to efficiently utilize and share the spectrum licensed by the primary user.We propose a cluster-based adaptive multispectrum sensing and access strategy, in which the secondary users seeking to access the channel can select a set of channels to sense and access with adaptive sensing time.Specifically, the spectrum sensing and access problem is formulated into an optimization problem, which maximizes the utility of the secondary users and ensures sufficient protection of the primary users and the transmitting secondary users from unacceptable interference.Moreover, we explicitly calculate the expected number of channels that are detected to be idle, or being occupied by the primary users, or being occupied by the transmitting secondary users.Spectrum sharing with the primary and transmitting secondary users is accomplished by adapting the transmission power to keep the interference to an acceptable level.Simulation results demonstrate the effectiveness of our proposed sensing and access strategy as well as its advantage over conventional sensing and access methods in terms of improving the achieved throughput and keeping the sensing overhead low.

Robustness is an important issue in tree based P2P streaming applications. In this paper, a new criterion to evaluate robustness is proposed. The criterion is derived from examining the weakest link of stream sources serving a peer. The quality of connection is defined as how complementary the connection is to the other existing connections in order to improve the completeness of the downloading stream as well as the backup copies. Peers can thus find better connection combination and experience the best stream quality and backup robustness from the existing connections. The collective maximization of robustness among peers rather than maximization of a peer's own resource alleviates the contention for resources, and helps the peers to reasonably allocate and utilize the limited network resources. Simulation results show marked performance improvement over other related proposals in terms of stream quality, speed of recovery from failure and ability to respond quickly to changing overlay and network dynamics.

Delay Tolerant Network (DTN) is a paradigm developed to cope with intermittent connectivity in wireless networks. Wireless networks are vulnerable to a variety of attacks, including wormhole attack. This paper proposes a statistical approach using infrastructure nodes to detect the presence of wormhole and localize the wormhole endpoints placement. The simulation results demonstrate that our mechanism is more effective than the related method called prohibited topology method, especially in high-speed network such as vehicular DTNs. The performance is independent of network density and node transmission range while there is a trade-off in performance when varying parameters namely node pause time and detection threshold.

This paper investigates network traffic before and after a vulnerability called Heart bleed becomes a public issue around March to May, 2014. To detect anomalies and potential threats due to the vulnerability, a wavelet entropy-based change-point detection method is proposed and compared with three other methods: prediction-based, clustering-based and Fourier transform-based. We show that the proposed wavelet entropy-based method outperforms the others in terms of ease of parameter setting, false alarm and detection accuracy. Using the proposed method and a visualization tool, we have studied Heart bleed vulnerability and successfully captured changes in packet volume and flow.

The Harn-Ren and Harn-Ren-Lin identity-based multisignature (IBMS) schemes are schemes derived from the RSA and GQ identity-based signature (IBS) schemes respectively. These IBMS schemes were claimed to be efficient based on two metrics - fixed length and constant verification time. This paper shows that both schemes suffer from similar flaws that allow adversaries to manipulate the list of signatories in a signature, with some of the attacks not requiring the adversaries to even possess valid signer keys. Such flaws render the schemes impractical for use in real-life where it can be safely assumed that they will be used non-atomically. Techniques to address the flaws are also discussed and a solution based on cryptography presented.

Cognitive radio is proposed as an efficient way to address the issue of spectrum shortage and under- utilization, in which cooperative spectrum sensing (CSS) is used to enhance the sensing performance. One of the most fundamental problems of CSS is: how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels? In this paper, We study the CSS problem under a more practical scenario where taking the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster- based CSS to obtain a proper assignment policy, in which all the cluster members cooperative in sensing the same channels, moreover, the CSS problem is formulated as a Maximum Weight One-Sided Biclique Problem, and a greedy heuristic algorithm is proposed to find the suboptimal assignment policy. To evaluate the tradeoff between sensing accuracy and spectrum opportunity, the simulation is conducted between the number of sensed channels and the achievable throughput.

Existing literatures show that deploying fixed relay infrastructure with stable interconnections can help to improve network connectivity. However, fixed relay infrastructure is inflexible and could be costly to deploy over large area. In this paper, we study the effectiveness of mobile relays in improving network connectivity in both Random Waypoint and Manhattan mobility models. Through extensive simulation, it is observed that mobile relays help to effectively improve the network connectivity, prolong the mean contact duration and reduce the number of network partitions. The mobile relay approach is also compared with the alternative approach of increasing node density. Through the simulation results, it is observed that mobile relay approach is far more effective than the alternative approach. Furthermore, we observe that the expected contact duration remains brief even with the mobile relays and discuss the implication of this observation in the design of routing protocols and clustering algorithms.

Nanonetwork, which is a new research area, is defined as a number of nanoscale components communicating and sharing information cooperatively. Recent research progress concerning nanonetwork includes carbon nanotube network, electromagnetic nanonetwork, molecular communication and so on. Molecular communication which comprises mobile or immobile biological nanomachines contributes to areas such as therapeutics, environment, food safety and agriculture. Calcium signaling employs calcium ion (Ca2+), which is one of the most important universal second messengers to spread information in tissues. Gap junction channel is formed by cell membranes of adjacent cells which enables calcium wave transmission. The permeability of gap junction can be regulated by external signals thus controlling the transmission of intercellular calcium waves. In this paper, we propose a cluster-based network model which is composed of cells and nanosensors utilizing the properties of calcium signaling. The characterizations and advantages of the network model are presented along with the comparison with the biological experiment results. The comparison shows that our proposed network model not only correctly simulates the biological behaviour but also shows how the biological communication can be enhanced via external intervention.

This paper proposes a Hybrid Protocol for Application Level Multicast with Dual Metric (HPAM-D) for live video streaming. HPAM-D constructs data distribution trees based on two performance metrics, namely, latency to source and loss rate experienced by the clients. The protocol is evaluated against the latency based single-metric HPAM and a modified HPAM-L which aims only to optimize loss rate regardless of the latency to source. HPAM-L acts as a control experiment in the evaluation. Another novel feature introduced in HPAM-D is the detection of likely local congestion via a heuristic based on the computation of a client's relative loss rate with respect to the data received by its parent as opposed to the commonly used absolute loss rate experienced by a client. Simulation results show that HPAM-D not only helps a client to reduce unnecessary parent switches thereby reducing protocol overheads but also maintains a low loss rate for its clients without compromising the RDP performance compared to the latency based, single-metric HPAM.

This paper describes an identity-based serial multisignature scheme that permits the maintaining of origin signer anonymity coupled with participant self-determination in a signature. It allows the origin signer to remain anonymous so long as it does not revoke its own anonymity by disclosing the proof of origin. The scheme also prevents an adversary from faking as the origin, or adding or deleting other participants into or from the multisignature, and is shown to be secure under the claims presented. Use cases for the scheme are presented to demonstrate the scenarios in which such a scheme is useful.

In this paper, we propose a PEP based Mail Proxy to enable mobile users to access email services over disruptive links. The proposed scheme offers a disruption tolerant proxy service for email client to communicate over intermittently connected links. The proposed scheme is transparent to the email client and requires no modification or configuration on the client. In addition, it enables the support for a large group of people without requiring individual configuration on each user device. The proposed protocol is implemented with SMTP support and a test bed is built. The performance result shows that it can deliver messages over disruptive links whereas direct IP connections fails.

MAC protocols to sense channels for data transmission have been widely investigated for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. One important issue associated with MAC protocols design is how the secondary users determine when and which channel they should sense and access without causing harmful interference to the primary user. In this paper, we jointly consider the MAC-layer spectrum sensing and channel access. Normal Spectrum Sensing (NSS) is required to be carried out at the beginning of each frame to determine whether the channel is idle. On detecting the available transmission opportunity, the secondary users employ CSMA for channel contention. The novelty is that, Fast Spectrum Sensing (FSS) is inserted after channel contention to promptly detect the return of the primary users. This is unlike most other MAC protocols which do not incorporate FSS. Having FSS, the primary user can benefit from more protection. A concrete protocol design is provided in this paper, and the throughput-collision tradeoff and utility-collision tradeoff problems are formulated to evaluate its performance. Simulation results demonstrate the efficiency of the proposed MAC protocol with FSS.

For better road safety and driving experience, content distribution for vehicle users through roadside Access Points (APs) becomes an important and promising complement to 3G and other cellular networks. In this paper, we introduce Cooperative Content Distribution System for Vehicles (CCDSV) which operates upon a network of infrastructure APs to collaboratively distribute contents to moving vehicles. CCDSV solves several important issues in a practical system, like the robustness to mobility prediction errors, limited resources of APs and the shared content distribution. Our system organizes the cooperative APs into a novel structure, namely, the contact map which is based on the vehicular contact patterns observed by APs. To fully utilize the wireless bandwidth provided by APs, we propose a representative-based prefetching mechanism, in which a set of representative APs are carefully selected and then share their prefetched data with others. The selection process explicitly takes into account the AP's storage capacity, storage status, inter-APs bandwidth and traffic loads on the backhaul links. We apply network coding in CCDSV to augment the distribution of shared contents. The selection of shared contents to be prefetched on an AP is based on the storage status of neighboring APs in the contact map in order to increase the information utility of each prefetched data piece. Through extensive simulations, CCDSV proves its effectiveness in vehicular content distribution under various scenarios.

In disruption tolerant networks (DTNs), nodes exchange beacon messages to set up links via a process known as neighbor discovery. However as it is, beacon messages are susceptible to forgery and tampering that could be exploited to attack and participate in the network. This paper outlines a protocol to 1) protect beacon messages in tactical-level DTNs from forgery and tampering utilizing an identity-based signature scheme (IBS), and 2) provide capabilities for nodes that are compromised or captured to signal its state discreetly to other DTN nodes. The protocol maintains the same amount of message overhead as without it.

In this paper, we address the energy-efficient contact discovery issue in Pocket Switched Networks (PSNs), in which the nodes' mobility pattern shows strong social property. In a PSN, although the end-to-end connection may be disconnected most of the time for a given source-destination pair, several nodes periodically gather at certain hot spots and form well connected clusters. Based on such mobility pattern, cooperation among nodes is utilized in our contact discovery design. The nodes that have already joined a cluster collaboratively wake up to discover new contacts. With local synchronization, the nodes can be operated in sleep mode more frequently, leading to high energy efficiency. Both the theoretical analysis and simulation results show that our cooperative duty cycling (CDC) greatly reduces the energy consumption while achieving comparable data delivery performance.

In this paper we propose MaxCD - a joint multi-flow scheduling and cooperative downloading protocol for drive-thru networks, with the goal of maximizing the amount of data packets that can be downloaded per drive-thru. Based on the macro-level opportunistic scheduling and node cooperation, the best wireless link(s) (with the highest data rate) between the roadside unit (RSU) and vehicular users are fully utilized. In addition, a multichannel collision-free relay mechanism is designed to address the reliable and fast data exchange issue when the vehicular users are outside the service area of the RSU. Our theoretical analysis vindicates the performance gain of the cooperation and extensive simulations demonstrate the efficiency of MaxCD.

In this paper, we propose a network based local mobility management scheme for 802.11 wireless networks. The scheme does not require modification on the mobile node and the mobility management is entirely handled by the network. Our scheme makes use of a cooperative DHCP service to ensure mobile node always obtains the same IP address within the mesh network. In addition, 802.11s is used to forward traffic to the correct location after mobile nodes moving to a different access point. We developed an analytical model to study the performance of proposed scheme and show that it can enable transparent node mobility in 802.11s networks. It can serve as an alternative to the existing proxy mobile IPv6 protocol when access points are under different domains.

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve wide network characteristics. Studies have shown that Small-World properties, primarily low average path length (APL) and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small-world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small-world properties show a degree of distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the APL and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using the Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on APL, clustering coefficient and connectivity.

This paper addresses the problem of delivering data packets for highly dynamic mobile ad hoc networks in a reliable and timely manner. Most existing ad hoc routing protocols are susceptible to node mobility, especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Routing (POR) protocol which takes advantage of the stateless property of geographic routing and the broadcast nature of wireless medium. When a data packet is sent out, some of the neighbor nodes that have overheard the transmission will serve as forwarding candidates, and take turn to forward the packet if it is not relayed by the specific best forwarder within a certain period of time. By utilizing such in-the-air backup, communication is maintained without being interrupted. The additional latency incurred by local route recovery is greatly reduced and the duplicate relaying caused by packet reroute is also decreased. In the case of communication hole, a Virtual Destination-based Void Handling (VDVH) scheme is further proposed to work together with POR. Both theoretical analysis and simulation results show that POR achieves excellent performance even under high node mobility with acceptable overhead and the new void handling scheme also works well.

A critical problem to peer-to-peer video-on-demand (P2P VoD) systems is to provide efficient user interactivity support. In this paper, we study intra- and inter-video operations separately, aiming to exploit the locality of reference in user access patterns and reduce the latency of these VoD operations. We first introduce the concepts of available, request and delivered locality in intra-video user access patterns and prove that high available locality exists in different videos by both simulation and theoretical analysis. Moreover, with a relaxed definition of data chunk holder, intra-video locality can facilitate a high likelihood of a peer seeking within a video, finding a holder of the requested data among its neighbors. Exploiting this property, an aggressive cached publish scheme is designed to build shortcuts over the DHT network so as to reduce the lookup delay. This scheme may be simple but it is practical and easy to implement. Inter-video locality is exploited via learning association rules from the collective viewing history. A fast association rule learning algorithm is proposed to infer the relations between videos in a distributed manner based on partial knowledge. Both search and content prefetch are incorporated to achieve low inter-video jump delay with minimal overhead. Our simulations demonstrate that the proposed schemes can reduce the buffer and lookup delay for seeking within a video and provide an efficient prediction-based prefetch scheme for inter-video access.

In a wireless domain where a mobile user accesses heterogeneous wireless technologies with multiple interfaces, a multi-path scheduling algorithm can benefit mobile users' experience by aggregating different network bandwidth together. However, existing literature actually shows that for TCP flows, it may not be the case. To better exploit multi-path scheduling for TCP connections, this paper presents a multi-path scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across different network access for these multi-homed users. Unlike other multi-path scheduling algorithms, ALBAM takes full advantage of the infrastructure of wireless domain and it has the following advantages: (1) it does not involve any upgrade of protocol stack on user devices. (2) ALBAM does not introduce any protocol signaling cost into the bandwidth-constrained wireless networks. (3) ALBAM reduces the number of out-of-order packets. Thus, ALBAM improves the throughput of the TCP connections. To evaluate the performance of ALBAM, we conduct comparative simulations of ALBAM against a related technique, i.e. Opportunistic Multipath Scheduling. The results show that ALBAM can achieve good bandwidth aggregation and provide better performance to TCP connections in the wireless domain.

For vehicle users, content distribution through roadside APs is an important complement to that through 3G and other cellular networks. However, the unique features of such access scheme pose many challenges on effectively satisfying vehicles' data request. This paper proposes a cooperative content distribution system for vehicles(CCDSV), based on the network-coding, vehicle-AP contact prediction, and data-prefetching techniques. CCDSV uses network coding to distribute encoded data for the effective sharing of popular contents. CCDSV predicts the vehicle's future contact APs and adaptively prefetches requested data in advance before vehicle's arrival. The proposed system is implemented into ns-2 simulator and demonstrates its effectiveness in terms of throughput, reduced traffic load, and download volume.

This paper addresses the downlink scheduling issue in drive-thru networks which is characterized by flow-level dynamics and user basis deadlines. Vehicular users requesting for data download service with variable file sizes regularly arrive at and depart from the limited coverage range of roadside access point. If the corresponding data queue at the access point cannot be serviced in a certain time period, it has to be cleared, resulting in degraded QoS. To minimize the number of uncompleted file download jobs in the face of multiple-user contention, a Dynamic Predictive Scheduling (DPS) algorithm is proposed. Based on the prediction of the remaining bandwidth of the different users, a scheduling tree is constructed to facilitate the selection of the data queue to serve at particular time slots. Through extensive simulation, it is shown that DPS consistently outperforms competitive scheduling schemes under varying workloads.

In this paper, optimal multiple channels cooperative spectrum sensing setting in non-identical environment is investigated. In previous work on cooperative sensing, all the secondary users have the same detection threshold and the noise received is independent identically distributed random variable. Thus researchers often assume that identical sensing time is assigned to the channels for spectrum sensing. In our paper, secondary users cooperatively sense the channel and send the binary results to the common receiver where energy detection with hard decision rule is employed. We assume that secondary users can assign individual sensing time to the channel with possibly different noise powers and detection thresholds. An iterative algorithm with polynomial complexity is established to determine the optimal sensing sequence for the secondary users to assign the mini-slots to the channels, such that the throughput increase can be maximized in each iteration. Furthermore, a new performance metric delay sensitivity is introduced to evaluate how long the authorized transmitting users need to wait for data transmission. Simulation results show that this iterative algorithm can yield better performance in cooperative spectrum sensing than other schemes with identical sensing setting in terms of both achievable throughput and delay sensitivity.

We look at the problem of network wide broadcast using the multi-rate feature of a wireless ad hoc network. Existing research has primarily focused on achieving minimum latency by construction of minimum weight connected dominating sets (WCDS) based on neighbourhood information. In this paper, we are interested in stateless multi-rate broadcasting algorithms in which nodes determine their broadcasting behaviour based on neighbourhood transmissions. The primary contribution of this paper is that we show how broadcast effectiveness at different data rates are related and how this relationship can be used to optimize algorithm design. We propose three stateless broadcasting algorithms and demonstrate the performance improvements achievable. Our simulation results show that significant benefits can be obtained in terms of minimizing both the number of forwarding nodes as well as the broadcast latency.

Delay Tolerant Network with New Serial Convergence Layer (DTN-NSCL) is a wireless serial RS-232 communication and networking protocol based on DTN2 reference implementation. Its novelty lies in the incorporation of multiple access control and neighbour discovery onto Delay Tolerant Network with Original Serial Convergence Layer (DTN-OSCL). This new aprotocol is able to provide long-haul communication and networking with high accomodation to common issue of environmental disturbances and interruptions. The authors have implemented a testbed to evaluate the performance of this new protocol. Experimental results show the success of the DTN-NSCL implementation as demonstrated in a long-range network using the Ultra High Frequency (UHF) band. The results also highlight the significant improvements over the DTN-OSCL where the former is able to discover its neighbours on the fly and support simultaneous serial communication among more than 2 nodes while the latter is only able to support point-to-point serial communication with pre-registered neighbours.

Unexpected disconnections, long transmission latencies and network heterogeneity that DTN is meant to support make the routing problem in DTN very complicated. In DTN, probabilistic based routing protocols make use of nodes' mobility history to gauge delivery likelihood of nodes. However, the delivery likelihood of the nodes is not the only factor that affects the message delivery likelihood. In this paper, a detailed analysis of routing in DTN reveals the forwarding decisions that could go wrong due to the store-and-forward nature of DTN. Based on the analysis, we propose a history of messages concept which Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET) can utilize to improve the chances of message delivery. Our simulations show that using the messages' history improves the message delivery performance of PRoPHET and is comparable to MaxProp.

Existing literature has looked to exploit wireless broadcast advantage (WBA) in order to optimize the performance of a wide variety of network operations. In this paper, we obtain a measure of WBA in a multihop scenario. We consider that all nodes in the network store and propagate implicitly received information from neighbourhood transmissions, resulting in the creation of a distributed cache, which we term broadcast cache. We obtain a lower bound on the growth of the broadcast cache in terms of the fewest set of transmissions in the network, which we define as the minimum set of non-altruistic transmissions. Subsequently, we use our results to obtain feasibility conditions that determine whether WBA can be effectively utilized depending on flow requirements.

A Trusted Platform Module (TPM) contains a unique identity and provides a range of security functions. This paper demonstrates a novel approach of using a TPM-enabled computer in a client-server system to hinder forensic examination. The prime motivation for this is to highlight the implications of such TPM-based approach in digital forensics for law enforcement agents. The system allows for data confidentiality, plausible deniability, and hiding of traces that data containing incriminating information was present on the client. The server will attest the client before allowing it to submit or receive encrypted data, and encrypted data containing incriminating information can only be decrypted 1) by the encrypting client, and 2) if and only if the encrypting client's platform configuration matches that during encryption. The client's state can always be established via measurement results, and they cannot be tampered to fake attestation.

Discrete cosine transform (DCT) has been proven to be a good approximation to the Karhunen-Loeve Transform (KLT) and has similar properties to the discrete Fourier transform (DFT). It also possesses a better energy compaction capability which is advantageous for speech enhancement. However, frame to frame variations of DCT coefficients even for a perfectly stationary signal can be observed. Therefore a DCT-based speech enhancement system with pitch synchronous analysis is proposed to overcome this problem. It reduces the drawbacks of fixed window shift and the amount of shift in the analysis window is now based on the pitch period, thus increasing the inter-frame similarities. Furthermore, a Wiener filter using the a priori signal-to-noise ratio (SNR) with an adaptive parameter is also derived and implemented as an advanced noise reduction filter. This proposed speech enhancement system is evaluated in terms of several objective measures and the experimental results demonstrate the good performance of the proposed system.

In this paper, we seek to provide reliable and fast content discovery in peer-to-peer (P2P) video-on-demand (VoD) system to enable user interactivity under peer dynamics. We first identify two characteristics of content discovery in P2P-VoD: real-time constraints and limited local cache. Tapping on these properties, we propose a hybrid content discovery mechanism: SUpeRchunk- based eFficient search Network (SURFNet). SURFNet divides movies into superchunks and chunks. Stable peers that are likely to have longer lifespans in the system are used to construct an AVL tree to provide superchunk-level data availability information. Peers storing the same superchunk are grouped into a holder-chain, which is then attached to a node in the AVL tree. This structured overlay is further extended by a gossip-based unstructured network for chunk-level information exchange and data transmission. Since the AVL tree consists of only stable peers, it provides a reliable backbone even in highly dynamic environment. Our analysis and simulation results show that SURFNet supports nearly-constant and logarithmic search time for seeking within a video and jumping to a different video, respectively.

Cooperative communication techniques have earlier been applied to design of the IEEE 802.11 medium access control (MAC) and shown to perform better. High rate stations can help relay packets from low-rate stations resulting in better throughput for the entire network. However, this also involves additional energy costs on the part of the relay which can result in reducing the network lifetime. We propose a cooperative MAC protocol NetCoop with the objective of maximizing the network lifetime and achieving high throughput. Based on this design, we also propose a flexible strategy which allows cooperation to be achieved using more than one relay. We show that this can achieve at least as good throughput as that of single relay cooperation while maintaining a high network lifetime.

Existing ad-hoc network routing strategies base their operations on flooding route requests throughout the network and choosing the shortest path thereafter. However, this typically results in a large number of unnecessary transmissions, which could be expensive for resource-constrained nodes such as those in a sensor network. In this paper, we propose a new mechanism HopAlert which optimizes route establishment and packet routing by limiting the number of nodes taking part in the route discovery process while achieving a low number of hops establishment. Using analysis and simulations, we show that this results in more routes with shorter hop counts than a reactive flooding protocol such as AODV while achieving higher savings.

In recent years, a lot of terminal mobility support schemes have been proposed. These schemes mainly focus on providing seamless mobility by reducing the handover delay. They work well when the handover takes place between two overlapping wireless networks. However, in some worst scenario, it is possible that a mobile node (MN) becomes disconnected from the network for some time before it can join another wireless network. In such case, existing mobility protocols will suffer great data loss and the data connection could break. The inability to preserve data connection across long handover delay or connectivity disruption is an unavoidable obstacle before a convergent terminal mobility can be achieved. In this paper, we propose a disruption tolerant mobility architecture (DTMA) to address this issue. In DTMA, terminal mobility is supported via the use of local proxy server. User applications communicate with other hosts via the local proxy on the mobile node itself. To enable disruption tolerance, DTMA adapts delay tolerant network (DTN) architecture as a transport protocol. When the node is disconnected from the network, data sent to it will be cached by the network and delivered to the node later when it reconnects to the network. The traffic overhead of DTMA is analyzed and it is shown that DTMA provides disruption tolerance with acceptable traffic overhead.

In this paper, we propose a novel Intrusion Detection System (IDS), Court-like Cluster-based IDS (CCIDS), to secure routing protocols in Mobile Ad Hoc Networks (MANETs). After the network is divided into one-hop clusters, each of these clusters performs similar functions as a court in real-life, such as accusation, investigation and defence. We show that court like IDS is effective in many aspects, especially the capability to prevent malicious alerts and reduce false positive rate. To further prove its effectiveness, we then apply CCIDS in securing the Optimized Link State Routing (OLSR) protocol to solve two most severe attacks-link spoofing and link deletion. Through extensive simulation, four performance parameters, namely, detection rate, false positive rate, detection delay, and communication overhead are evaluated.

As a local mobility management protocol, Proxy Mobile IPv6 (PMIPv6) is designed to enable mobile nodes to move in a domain without any involvement in the mobility signalling operation. However, a single domain cannot satisfy mobile nodes' movement requirement. Hence, we propose a solution to enable mobile nodes to move across multiple PMIPv6 domains. We introduce a new network entity called traffic distributor (TD) to manage the inter-domain handover. Through redirecting the traffic to the domain which currently visited by the mobile node, TD can ensure mobile node continue its session even when it moves across domains. We conduct a series of experiments to compare our proposal with Neumann proposal which is another proposal to handle inter-PMIPv6-domain issues. Experimental results show that our proposal is a viable alternative for inter-domain handover, it can outperform Neumann proposal in terms of binding cache entry numbers,transmission delay and handover delay.

A critical problem to P2P VoD systems is to provide efficient user interactivity support. In this paper, we study intra- and inter-video operations separately and aim to reduce the latency of these VoD operations by exploiting the locality of reference in user access patterns. With a relaxed definition of data chunk holder, intra-video locality can facilitate a high likelihood of a peer seeking within a video, to find a holder of the requested data among its neighbors. Tapping on this property, an aggressive cached publish scheme is designed to build shortcut over the DHT network so as to reduce the lookup delay. Inter-video locality is exploited via learning association rules from the collective viewing history. A fast association rule learning algorithm is proposed to infer the relations between videos in a distributed manner based on partial knowledge. Both search and content prefetch are incorporated to achieve low inter-video jump delay with minimal overhead. Our simulations demonstrate that the proposed schemes can reduce the lookup delay for seeking within a video and provide an efficient prediction-based prefetch scheme for inter-video access.

In this paper, we study the throughput and delay scaling laws of two coexisting mobile networks. By considering that both the primary and secondary networks are mobile and move according to random walk model, we propose a multi-hop transmission scheme. Based on the assumption that the secondary node can help to relay the primary packet, we show that the secondary network can achieve the same throughput and delay scaling laws as in stand-alone network Ds(m)=Θ(mλs(m)). Furthermore, for primary network, it is shown that the throughput-delay tradeoff scaling law is given by Dp(n)=Θ(√(n log n λp(n))), when the primary node is chosen as relay node. If the relay node is a secondary node, the scaling law is Dp(n)=Θ(√(nβlog n λp(n))), where β>;1. The novelties of this paper lie in: i) Detailed study of the delay scaling law of the secondary network in the complex scenario where both the primary and secondary networks are mobile; ii) The impact of buffer delay on the primary and secondary networks due to the presence of preservation region. We explicitly analyze the buffer delay and obtain an expression as DII(SR)(m)=Θ(1/√(nβ-1as(m))).

Traditional MANET routing protocols are quite susceptible to nodes' mobility, especially for large-scale networks in which the end-to-end path length is usually large. In order to improve the routing performance in the face of fast changing network topology, we propose a novel Robust Geographic Routing (RGR) protocol which takes advantage of the broadcast nature of wireless medium by employing opportunistic routing like forwarding strategy. At the same time, a Virtual Destination based Void Handling (VDVH) scheme is also proposed to work together with RGR. Simulation results show that RGR achieves excellent performance even under high node mobility and the new void handling scheme also works well and further enhances the performance of RGR.

Despite the quality improvement of the speech signal with most traditional noise reduction (TNR) algorithms, the output is always distorted to some extent due to the over-attenuation of speech components. Weak speech components are usually regarded as noise in noise reduction processing and are therefore highly suppressed. In this paper, we propose a postprocessing technique which is based on the regeneration of both the voiced and unvoiced speech in the entire frequency domain to reduce this problem. A nonlinear transform is first applied to obtain the excitation signal, and a smooth envelope is then estimated. To utilize the information of the clean speech contained in the envelope, we combine the original TNR filter output with a weighted product of the excitation signal and the estimated envelope to generate the final synthesized speech. The synthesized speech is quite close to the clean speech and is more natural-sounding. Moreover, our algorithm can mask the residual musical noise effectively with the regenerated speech components. Experimental results demonstrate the excellent performance of our algorithm. In addition, we introduce two novel objective measures and further show the efficiency of our algorithm in maintaining the clean speech while reducing the noise as much as possible.

It is crucial to provide reliable and fast search in P2P VoD system to enable user interactivity under peer dynamics. We propose a hybrid content discovery mechanism: superchunk based fast search network (SURFNet), based on our observations of current P2P VoD systems. SURFNet divides movies into superchunks and chunks, and takes advantages of stable peers to construct an AVL tree, which provides superchunk-level data availability information and aggregates superchunk holders into holder-chains. This structured overlay is further extended by a gossip-based unstructured network for chunk-level information exchange and data transmission. Our analysis and simulation results show that SURFNet supports efficient search and reduces seek latency significantly.

Traditional MANET routing protocols are quite susceptible to link failure as well as vulnerable to malicious node attack. In this paper, we propose a novel protocol called Position based Opportunistic Routing (POR) which takes full advantage of the broadcast nature of wireless channel and opportunistic forwarding. The data packets are transmitted as a way of multicast (which is actually implemented by MAC interception) with multiple forwarders. A forwarder list determined by previous hop according to local position information is inserted into the IP header and the candidates take turn to forward the packet based on a predefined orders. This redundancy and randomness make it quite efficient and robust. In addition, inherited from position based routing, POR's control overhead is almost negligible which justifies its good scalability. Both theoretical analysis and simulation results show that POR not only achieves outstanding performances in normal situations but also yields excellent resilience in hostile environments.

Despite the success of recent speech enhancement algorithms, the enhanced signals still suffer from undesirable speech distortion caused by over-attenuation of weak speech spectral components. In this paper, a post-processing technique based on the regeneration of both voiced and unvoiced speech is proposed to alleviate this problem. A non-linear transformation is first applied to aWiener filtered speech and the transformed signal is multiplied by a pre-estimated spectral envelop to form the regenerated speech. The resulting speech is then obtained using a weighted combination of the regenerated speech components and the filtered speech. This process significantly improves the resulting speech quality as compared to the original filtered version. It results in speech that sounds less lowpassed. Also, the residual musical noise is significantly masked by the regenerated speech components. Objective measures show that the quality of the resulting speech is much closer to the clean speech as compared to the original Wiener filtered speech.

Significant throughput degradation of multihop path communication in wireless mesh network is one of the major problems in wireless communication. The main reason for the lack of bandwidth is channel interference, which is caused by contention for the shared channel between wireless nodes. The natural approach to overcome this problem is exploiting the availability of multiple channels multiple interfaces (MCMI) networks. However, it is costly and may not be practical to dedicate one interface per channel for every node. Thus in this paper, we study the MCMI network, where the number of interfaces that every node has is less than the number of available channels. Simple and distributed channel scheduling algorithms for communication in multiple channels multiple interfaces networks are discussed. The objective of the proposed algorithms is to minimize the channel interference that causes the throughput degradation in multihop networks. The proposed algorithms are evaluated with extensive simulations. The simulation results show that the proposed algorithms well exploited the availability of multiple channels multiple interfaces to overcome the throughput degradation problem.

To improve the local mobility performance of a wireless network, we present a new framework, namely Efficient Framework for Local Mobility (EFLoM). In EFLoM, we introduce three entities: Local Anchor Router (LAR), Wireless Access Gateway (WAG) and Mobile Node (MN). LAR is a router that is in charge of MN's IP mobility management. It keeps track of MN's movement, and delivers the MN's packets to its current location. A WAG is an access router that is responsible for managing the traffic flows within the same local mobility domain. In EFLoM, when Corresponding Node (CN) and MN connect to the same local mobility domain, packets are sent directly between CN and MN by WAG, without suffering sub-optimal routing problems. A MN is a mobile host which updates its data packets' header with its current location address before packets are sent out. To evaluate our new framework, we compare EFLoM with Hierarchical Mobile IPv6 Mobility Management (HMIPv6) which is an existing protocol that has good performance for local mobility. Both analytical analysis and simulation result show that EFLoM can attain substantial improvements over HMIPv6 in terms of handover delay, end to end delay, traffic overhead, which therefore presents EFLoM as a new engineering alternative to existing MIPv6 based techniques.

Communications in single channel single interface ad hoc network suffer from channel access contention, which results in bandwidth scarcity. One way to overcome this problem is through the use of multiple channels multiple interfaces (MCMI). In this paper, we present a dual-interface multiple channels destination-sequenced distance-vector (DSDV-M) routing protocol, which is the extension of the DSDV routing protocol to MCMI version. The objective of the proposed algorithm is to reduce the channel interference so that multiple transmissions can occur concurrently, thus increasing network capacity. The proposed algorithm is evaluated against channel scheduling algorithms; the simulation results show that the proposed algorithm is able to exploit the availability of multiple channels multiple interfaces to improve network capacity. In addition, we are also able to gain greater than twice the throughput of DSDV with single channel single interface.

Mobile IP enables IP mobility support for mobile node (MN), but it suffers from triangular routing, packet redirecting, increase in IP header size, and the need for new infrastructure support. This paper details an alternative to enable terminal mobility support for MN. This scheme does not suffer from triangular routing effect and does not require dedicated infrastructure support such as home agent. It also does not increase the size of the IP header and does not require redirection of packets. These benefits are enabled with a tradeoff, which requires modifications on MN and its correspondent node. It uses an innovative IP-to-IP address mapping method to provide IP address transparency for applications and taps on the pervasiveness of SIP as a location service. From our analysis, we show that TMSP is much more efficient than mobile IP in terms of the number of hops as well as overhead. Our prototype implementation also shows that TMSP provides seamless communication for both TCP and UDP connections and the computational overhead for TMSP has minimal impact on packet transmission.

In this paper, we present a comparative performance evaluation of our proposed neighbor discovery scheme versus other basic neighbor discovery schemes based on omnidirectional broadcast. The scheme is able to discover nodes beyond the omnidirectional transmission range with the help of minimal number of neighbors. It does not require high-power broadcast and works in a distributed manner. Simulation results show that the proposed scheme is able to discover neighbors faster than other basic neighbor discovery schemes.

In recent years, wireless communication technologies have been pervasive. We have many wireless options to connect to the Internet: IEEE 802.11 WLANs, GPRS, UMTS and many other competing technologies. These wireless standards were designed independently of each other. Hence, these standards do not specify a common solution to perform connection handover between two different standards. Given that each of these standards has its own advantages and disadvantages, users may want to use the best available connection for their needs. In this paper, we propose a scheme that enables seamless transitions between different wireless standards through SIP protocol. With our scheme, IP mobility across both vertical and horizontal handovers is possible without any modification to the existing applications. We define a guideline for handovers between networks, and then perform experiments with our scheme implementation to confirm the feasibility of our proposed scheme. Our results show that with our scheme, connection disruption is minimized.

A critical problem for P2P streaming applications is to construct and maintain the overlay such that it continues to efficiently distribute data stream even in dynamic network environment. A common solution to this problem is to constantly adapt the overlay structure to the changing network conditions. However, in this paper, we propose an algorithm to schedule the sending order of queued data at each peer by taking into account both the data content as well as the overlay conditions. The data packets which are important to most users on the entire P2P system are sent out earlier. Simulation results show that the scheduling algorithm improves the overall streaming quality of the P2P system with little overhead added to the network traffic. Moreover, the improvement in overall streaming quality is also achieved regardless of the video streaming formats.

In this paper, we propose a mobility management scheme with quality-of-service(QoS) support for heterogeneous multihomed mobile node. With this scheme, the multihomed mobile node can move between networks seamlessly and at the same time, utilize all available links based on the QoS requirement. Session initiation protocol(SIP)[l] is used in session management and each multihomed node is identified by a unique SIP uniform resource identifier(URI). This mobility scheme supports both horizontal and vertical handover and no triangular routing is involved. Depending on the QoS requirement, each data flow can either be allocated to one of the links or distributed across multiple links. The ability to distribute data at packet level across multiple links is unique compared to many approaches using flow level distribution. The proposed scheme not only effectively increases the throughput and reduces the delay, but also alleviates out-of-sequence packet problem where certain applications are sensitive to.

The IEEE 802.11e standard has introduced specifications for service differentiation among different classes of data by specifying four service classes and a new contention resolution mechanism called EDCA. However, while the protocol shows a better performance for higher priority data such as voice and video, the performance is seen to drop drastically at high loads. In this paper, we explore the effectiveness of a multi-stage contention scheme for providing QoS differentiation among four different service classes, as specified by EDCA. From our analysis, we observe that the multi-stage with prioritization that we propose gives a much better performance than EDCA for higher priority data. Moreover, its good performance even at high network loads shows that this design is much more scalable.

An IP mobility support protocol that enables personal and terminal mobility for IP-based applications is put forward. This protocol does not require new network entities or support from network service providers. It comprises an innovative IP-to-IP address mapping module at the network layer and an user agent to interact with a directory service server and correspondent nodes. It does not require a permanent IP address and a home server. It does not use tunnelling on mobile nodes nor alter route path of IP packets. In this paper, we describe our implementation and present our experimental results. Experiments show that this protocol works for UDP and TCP connections without affecting the throughput of the mobile node on a wireless LAN. Related works are also discussed and quantitatively compared. As an example, this protocol provides seamless execution for applications like VoIP and video conferencing on mobile nodes that roam across wireless networks.

A hybrid protocol for application level multicast (HPAM) for live video streaming without native IP multicast support is proposed. HPAM exploits the simplicity and optimality of a lightweight, centralized server with the robustness and scaleability of distributed clients. HPAM self-organizes clients on the fly to form efficient source-based overlay trees while the server facilitates peer discovery and also serves as a reliable backup should the distributed algorithms fails. Tree construction, refinement and recovery from partitions are carried out independently by the clients. Simulation results show that HPAM can build and maintain reasonably latency-efficient overlay trees with a lower overhead than a fully centralized system (host based multicast) and yet more responsive to group dynamics and network environment than a fully distributed system (host multicast).

Peer-to-peer (P2P) structure is widely applied in multimedia streaming applications to support large number of clients spread all over the Internet. For real-time streaming applications, data arrival time is critical. In this paper, we design a scheduling scheme to manipulate the order of data transmission between peers in order to improve the transmission efficiency in P2P streaming. Simulation results show that our scheme improves average data arrival time and increases the rate of data arriving on time in dynamic fluctuating network.

A working prototype of a building identification service which can be used on any camera cell phones equipped with GPS capability has been developed. Users can simply snap photos of architectures and send them, together with the corresponding GPS coordinates, via MMS to a remote server. The server will match the photos with the stored, GPS-tagged images using a combination of scale saliency algorithm for feature matching and earth movers distance measure for scene matching. The estimated location and other information are then sent back to the users via MMS. This prototype will have better accuracy than systems which rely solely on photo recognition given the exploitation of GPS information. Moreover, it is computationally lighter since the recognition engine only needs to compare stored images which lie within the GPS coordinates error range. It is relatively inexpensive as no special phones or subscriptions to telecommunication providers for provision of GPS equivalent location data (i.e.cell location) are needed.

In network mobility support for wireless ad hoc networks, mobile routers (MRs) are connected to access router (AR) by ad hoc routing. Mobile network nodes (MNNs) connect to an MR to communicate to distant correspondent nodes (CNs). Previous works on network mobility in mobile IPv6 involve packet redirection through a home agent; this redirection is costly in both bandwidth and delay. In this paper, we propose a terminal-assisted network mobility management scheme, which requires neither permanent node's IP address nor additional network servers/infrastructure support. With this scheme, IP packets redirection is not necessary; IP packets are routed directly between MNNs and CNs. Our scheme is implemented in three components: an IP-to-IP address mapping scheme on the MNN, an IPv6 header extension on each IP packet, and an IP address redirection scheme on the MRs. An MNN is located by a uniform resource identifier (URI) provided by a directory service that maps URI to IP address. Through numerical analysis, we show that our mobility management scheme exhibits better efficiency compared with that of mobile IPv6.

Latency-rate (LR) schedulers have shown their ability in providing fair and weighted sharing of bandwidth with an upper bound on delivery latency of packets while earliest departure first (EDF) schedulers have shown their ability in providing LR-decoupled service whereby the delivery latency of packets is not bounded by the reserved rate. However, EDF schedulers require traffic shapers to ensure flow protection. We propose quantum-based earliest deadline first scheduling (QEDF), a quantum-based scheduler that provides flow protection, throughput guarantee and delay bound guarantee for flows that require LR-coupled and LR-decoupled types of reservations. It classifies flows into time-critical (TC), jitter-sensitive (JS), and rate-based (RB) classes and uses a quality-of-service forwarding rule to determine the next packet to be serviced by the scheduler. It provides nonpreemptive priority service to TC queues. This allows LR-decoupled reservation for flows that have a low rate and intolerable delay. Packets from JS queues can be delayed by other packets if forwarding the latter will not result in the former missing its deadline. As a quantum-based scheduler, the QEDF scheduler provides throughput guarantees for RB queues. We present both analytical and simulation results of QEDF, whereby we evaluated QEDF in its deployment as a single-class as well as a multiservice scheduler

We apply the mobility support of IPv6 to a wireless network where flat ad hoc routing protocol is used for interconnecting mobile routers. The use of flat routing protocol removes the need for more than one level of recursive nesting to form a wireless network backbone. We introduce a redirect header for mobile IPv6, which reduces the overhead of tunnelling by half in wireless network where bandwidth is limited. Through numerical evaluations, we show that this header aids in reducing the tunnelling overhead. We also discuss the current route optimisation proposals and their applicability to our network scenarios.

The Internet's increasing popularity and widespread acceptance have prompted its use as an alternative medium for fax, voice, and video communications to reduce cost by circumventing expensive international toll rates. Unified messaging integrates different media such as facsimile, text mail, voice mail, video mail, and pager-messages into a single mechanism for message submission, transportation, and retrieval. In this paper, a unified messaging environment over the Internet is proposed. This environment essentially comprises two gateways, namely, the front end gateway FEG and the back end gateway BEG to provide the necessary support for message dispatch and retrieval. Front end gateway is responsible for the message dispatch process including submission, preprocessing, packaging, and Internet delivery, while BEG uses a dual-contact technique for delivery of the unified message to the recipient over packet switched telephone network and the Web. The two gateways can be combined into a unified messaging gateway UMG system.