LiDAR is one of the most important sensors in the field of autonomous driving and allows for better and more accurate perception of the changes in the surrounding environment. Most of the existing 3D object detection methods use data augmentation and feature fusion enhancement to improve the performance of detection, but the majority of the methods ignore the handling of sample imbalance problems during data augmentation. Also, the designed feature fusion and enhancement methods were not well suited to work with the enhancement methods. To this end, we developed a combined method involving data augmentation and feature enhancement. The designed approach has two main objectives: 1) to address the problem of unbalanced sample distribution in detection scenes through data augmentation, and 2) to enhance feature perception using a special feature enhancement module. Our proposed method solves the problem of class imbalance by directly increasing the number of pedestrian samples in the scene through mixed data augmentation, i.e., RG-Aug. In addition, we introduce the Decoupling and Attention Fusion module (DAF), which combines classification headers with high-level features and prediction branches with low-level features. Leverage data features between different layers of features to get a more robust feature representation. Finally, the multi-scale pyramid attention enhancement module is designed to achieve feature enhancement of multi-scale features by means of attention to improve the detection ability of small objects in the scene, especially the detection ability of pedestrians. Our method can achieve 1.57%, 2.16%, and 2.05% performance improvement on the KITTI dataset for Easy, Mod, and Hard samples, respectively. Furthermore, for the detection of pedestrians, our method has a significant competitive advantage over other state-of-the-art techniques with a mAP of 73.42%.

Unsupervised anomaly detection in multivariate time series can prevent large-scale system failures and is crucial for various applications. Most existing methods only consider a single temporal pattern and insufficiently model the normal pattern, causing the model to learn incorrect temporal patterns from anomalous or noisy data. This poses a significant challenge for accurate anomaly detection. To overcome these challenges, we introduce a new dynamic decomposition and reconstruction anomaly detection algorithm, DMRAD. DMRAD captures various regular patterns of multivariate time series by designing a dynamic decomposition module that learns trend and seasonal features. By integrating improved channel and temporal attention mechanisms, DMRAD effectively learns the correlations within the sequence and dependencies across different sequences, thereby enhancing the model's capacity to distinguish between features and extract relevant information. DMRAD incorporates a latent anomaly-noise detection algorithm to identify and suppress the influence of noise and latent anomalies, elevating the overall accuracy of anomaly detection. Extensive experimental comparisons demonstrate that DMRAD achieves state-of-the-art performance on a variety of datasets for real-world application scenarios.

The exponential growth of mobile network traffic makes accurate traffic prediction essential for network optimization. However, this remains challenging due to complex spatial-temporal dependencies and diverse traffic patterns across base stations. Existing methods often rely on single-factor modeling or fail to effectively handle this multi-pattern heterogeneity. We propose the Meta-Optimized Multi-Period Spatial-Temporal Transformer (MMSTT), a novel framework integrating dynamic graph-based spatial modeling and multi-period temporal fusion within a Transformer architecture. To address the multi-pattern nature of traffic data, we incorporate a clustering algorithm and a meta-learning optimization process. This process captures shared features across patterns during meta-training and adapts to pattern-specific characteristics during meta-testing. Experimental results on a real-world dataset demonstrate that MMSTT significantly outperforms existing baselines, reducing the mean absolute error (MAE) to 28.39, root mean square error (RMSE) to 42.83, and mean absolute percentage error (MAPE) to 0.37. Compared to the standard Transformer and GCN baselines, MMSTT achieves improvements of 38.71% in MAE, 33.57% in RMSE, and 60.22% in MAPE.

Current low-light face detection usually enhances images first and then detects faces. Image enhancement focuses on the global enhancement of the whole image and is suitable for the human perspective. However, face detection requires high quality of local face regions and has different optimization objectives from image enhancement task. Such mismatches between the two tasks result in inferior performance of low-light face detection. To solve these obstacles, we propose end-to-end low-light face detection through global and local enhancement. Specifically, the proposed method consists of two components, i.e., image enhancement and face detection. For image enhancement, both global and local enhancement are explored. Global enhancement utilizes illumination constraints to enhance the overall image illumination, while local enhancement employs a generative adversarial network to improve the illumination of face regions during training. Through a combination of global and local enhancement, we reduce the gap between image enhancement and face detection and optimize the two tasks jointly. Furthermore, regions of the human body may help face detection, especially when face regions are small and blurred. Therefore, we regard body region as context to assist face detection. We use the context assistance module and optimize the annotation of the body regions with the aid of human structure prior knowledge. Experiments on the Dark Face dataset demonstrate the effectiveness of the proposed method.

Task offloading in cognitive radio-enabled Internet of Vehicles (CIoV) networks faces critical challenges due to the coupling of vehicular mobility, spectrum dynamics induced by primary users (PUs), and delay-sensitive service constraints. Existing approaches often decouple task offloading, spectrum access, and resource allocation, resulting in poor adaptability under dynamic and dense traffic conditions. To address these limitations, this paper proposes a Robust Parallel Benders Decomposition (RPBD) framework for mobility-aware task offloading in CIoV networks with bidirectional traffic and stochastic spectrum availability. The proposed framework introduces three key innovations: (1) a parallel decomposition strategy that separates discrete offloading decisions from continuous resource allocation, enabling scalable optimization; (2) a staleness-controlled cut generation mechanism that ensures solution quality under asynchronous updates; and (3) an adaptive parameter adjustment strategy that dynamically accommodates vehicular mobility and PU-induced spectrum variability. The formulation incorporates bidirectional mobility through service time windows and models spectrum availability using Markov-modulated channel states. Theoretical analysis guarantees convergence through convex subproblem structures and martingale-bounded parameter evolution. Extensive simulation results demonstrate that RPBD reduces task latency by 62.7 % and outage probability by 41.9 % compared to conventional Benders decomposition. Moreover, it maintains latency below 300 ms under 60 km/h mobility and achieves 83.4 % spectrum utilization efficiency in urban CIoV scenarios.

Although the descriptions of facial action units (AUs) provide crucial semantic knowledge for representation learning from facial images, they have not been fully explored for facial action unit recognition. In this paper, we propose a method that effectively explores the knowledge existing in AU descriptions to enhance AU recognition. Specifically, the proposed method consists of three components, i.e., AU recognition network, global representation alignment, and AU representation alignment. The AU recognition network extracts global features and AU-specific features for AU prediction from images. To leverage AU textual descriptions fully, we design two-level representation alignment for AU recognition. The global representation alignment component closes the distance between the global facial features and its corresponding positive global embedding extracted from textual descriptions. Then, the AU-specific features are aligned with the positive AU textual embedding by the AU representation alignment component. Negative textual embedding generation strategies are also designed to further boost the two-level representation alignment. Through the two-level alignment, AU textual descriptions guide image representation learning of the AU recognition network. Experiments on two benchmark datasets and one in-the-wild dataset demonstrate the efficacy of the description-enhanced AU recognition method, compared with the state-of-the-art works.

Despite remarkable advancements in empathetic response generation (ERG) area, existing research has centered on achieving affective and cognitive empathy by perceiving users' emotions and deducing contextual information from knowledge databases. Human communication combines textual, visual, and audio cues to interpret other's intentions. However, previous ERG works have focused on text-based methods and neglected contextual information within audiovisual data. To bridge the gap, we propose fostering empathy with users by integrating audiovisual and text modalities. First, the proposed method uses a cross-modal attention mechanism to perceive users' emotions from the multi-modal conversation. It integrates multi-modal data with the perceived emotions during response generation process, so that the generated responses resonate with users at the affective level by mirroring their emotions. Second, we import guidance text that focuses on visual context or user experiences and provides contextual information, thus enhancing cognitive empathy. The proposed method aligns multi-modal dialogue history and guidance text through the multi-source attention mechanism. Finally, the proposed method produces empathetic responses by understanding users' backgrounds and emotions. Experiments on three multi-modal datasets, e.g., MELD, IEMOCAP, and MEDIC, demonstrate that the proposed method outperforms state-of-the-art works.

Mobile opportunistic networks (MONs) have gained considerable attention in enabling impromptu communication due to the progress in communication technologies over the past decade. One of the main characteristics of MONs is their inconsistent connectivity and implicit end-to-end routing paths. This presents a challenge in making optimal routing decisions during message transmission, as we need to identify relay nodes that can reliably transport messages to their destinations quickly and cost-effectively. Here, we propose a novel multi-criteria fusion routing decision making algorithm (MFRD). Initially, we establish four metrics that measure the message transmission capability (MTC) of a node in terms of message throughput, message tolerance, geographic connectivity, and time connectivity. Next, we use the game theory mechanism to merge the DEMATEL subjective weights and the CRITIC objective weights of each metric, resulting in a more thorough integrated weight for them. Then, we integrate the benefits of two MCDM techniques, TOPSIS and GRA, to provide a new relative proximity measure for evaluating the MTC of alternative nodes. Finally, during message propagation, we select nodes with higher MTC as next-hop relays. The simulation results demonstrate that MFRD not only significantly improves the delivery rate and reduces the average latency, but it also shows excellent performance in network overhead and average hop count.

Opportunistic Mobile Social Networks (OMSNs) represent a unique class of Delay-Tolerant Networks (DTNs) comprised of mobile nodes equipped with communication devices. Data transmission within these networks hinges on interactions between mobile nodes, characterized by unstable connectivity and frequent node mobility. Existing DTN routing algorithms, which incorporate social attributes such as degree centrality, contact frequency, and connection duration, build upon traditional approaches. However, many of these algorithms overlook the mobile social characteristics of nodes, limiting their practical applicability in real-world scenarios. In this work, we utilize the SMOOTH and SPMBM mobility models to simulate realistic movement trajectories in a temporary shelter context, while deeply exploring the mobile social attributes of nodes. We propose a methodology for categorizing friend relationships based on node contact scenarios and predict the mobility of message destinations using node friendships. Our validation results indicate a prediction accuracy of 70% in a GPS-denied environment. Furthermore, through rigorous controlled experiments, our approach has demonstrated superiority over comparable algorithms in terms of delivery rate, network load, and forwarding hop counts.

Delay-Tolerant Networks (DTNs) are commonly utilized in the challenging environment, serving as a valuable complement to conventional networks. In such a context, energy efficiency is of paramount significance. The existing researches on energy saving algorithms in DTNs neglect the influence of different stages of message transmission on the energy consumption. This paper categorizes node energy consumption into two distinct categories, namely, basic and effective energy consumption. On this basis, a segmentation Energy-Saving routing strategy based on Effective Energy Consumption Perception (EECP-SES) is further developed. The strategy segments the service status of nodes, and enhances the scanning interval function of the exponential distribution function via regulating the basic energy consumption, monitoring the effective energy consumption rate, and introducing an energy consumption balance mechanism. The EECP-SES strategy was then implemented on two classical routing algorithms in the conducted simulation experiments. Experiments have demonstrated that the implementation of the EECP-SES yields favorable outcomes in terms of prolonging network service duration and achieving successful message delivery. Compared with six other strategies, EECP-SES strategy helps the Epidemic algorithm extend network service time by 26.57% to 104.89%, helps Prophet algorithm extend network service time by 42.56% to 114.67%.

Although video affective content analysis has great potential in many applications, it has not been thoroughly studied due to limited datasets. In this paper, we construct a large-scale video affective dataset with danmu (VAD). It consists of 19,267 elaborately segmented video clips from user-generated videos. The VAD dataset is annotated by the crowdsourcing platform with discrete valence, arousal, and primary emotions, as well as the comparison of valence and arousal between two consecutive video clips. Unlike previous datasets, including only video clips, our proposed dataset also provides danmu, which is the real-time comment from users as they watch a video. Danmu provides extra information for video affective content analysis. As a preliminary assessment of the usability of our dataset, an analysis of inter-annotator consistency for each label is conducted using weighted Fleiss' Kappa, regular Fleiss' Kappa, intraclass correlation coefficient, and percent consensus. Besides, we also perform a statistical analysis of labels and danmu. Finally, video affective content analysis is conducted on our dataset and three typical methods (i.e., TFN, MulT, and MISA) are leveraged to provide benchmarks. We also demonstrate that danmu can significantly improve the performance of the video affective content analysis task on some labels. Our dataset is available for research purposes.

Although expression descriptions provide additional information about facial behaviors despite of different poses, and pose features are beneficial to adapt to pose variety, neither has been fully leveraged in facial expression recognition. This paper proposes a pose-aware text-assisted facial expression recognition method using cross-modality attention. Specifically, the method contains three components. The pose feature extractor extracts pose-related features from facial images, and then cooperates with a fully-connected layer for pose classification. When poses can be clearly discriminated and classified, features obtained from the extractor can represent the corresponding poses. To eliminate bias due to appearance and illumination, cluster centers are taken as the final pose features. The text feature extractor obtains embeddings from expression descriptions. These descriptions are first passed through Intra-Exp attention to obtain preliminary embeddings. To leverage the correlations among expressions, all expression embeddings are then concatenated and passed through Inter-Exp attention. The cross-modality module attempts to learn attention maps that distinguish the importance of facial regions by using prior knowledge about poses and expression descriptions. The image features weighted by the attention maps are utilized to recognize pose and expression jointly. Experiments on three benchmark datasets demonstrate the superiority of the proposed method.

Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in the coarse-grained traffic graph can guide feature learning in the fine-grained traffic graph. To this end, we propose a Spatiotemporal Multiscale Graph Convolutional Network (SMGCN) that explores spatiotemporal correlations on a multiscale graph. Specifically, given a fine-grained traffic graph, we first generate a coarse-grained traffic graph by graph clustering, and extract spatiotemporal correlations on both fine-grained and coarse-grained traffic graphs. Then we propose a cross-scale fusion (CF) to implement information diffusion between the fine-grained and coarse-grained traffic graphs. Moreover, we employ an adaptive dynamic graph convolution network to mine both static and dynamic spatial features. We evaluate SMGCN on real-world datasets and obtain a$1.18\% -3.32\%$improvement over state-of-the-arts.

Existing methods for facial expression recognition (FER) lack the utilization of prior facial knowledge, primarily focusing on expression-related regions while disregarding explicitly processing expression-independent information. This paper proposes a patch-aware FER method that incorporates facial keypoints to guide the model and learns precise representations through two collaborative streams, addressing these issues. First, facial keypoints are detected using a facial landmark detection algorithm, and the facial image is divided into equal-sized patches using the Patch Embedding Module. Then, a correlation is established between the keypoints and patches using a simplified conversion relationship. Two collaborative streams are introduced, each corresponding to a specific mask strategy. The first stream masks patches corresponding to the keypoints, excluding those along the facial contour, with a certain probability. The resulting image embedding is input into the Encoder to obtain expression-related features. The features are passed through the Decoder and Classifier to reconstruct the masked patches and recognize the expression, respectively. The second stream masks patches corresponding to all the above keypoints. The resulting image embedding is input into the Encoder and Classifier successively, with the resulting logit approximating a uniform distribution. Through the first stream, the Encoder learns features in the regions related to expression, while the second stream enables the Encoder to better ignore expression-independent information, such as the background, facial contours, and hair. Experiments on two benchmark datasets demonstrate that the proposed method outperforms state-of-the-art methods.

Although empathic interaction between counselor and client is fundamental to success in the psychotherapeutic process, there are currently few datasets to aid a computational approach to empathy understanding. In this paper, we construct a multimodal empathy dataset collected from face-to-face psychological counseling sessions. The dataset consists of 771 video clips. We also propose three labels (i.e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients. Expression of experience describes whether the client has expressed experiences that can trigger empathy, and emotional and cognitive reactions indicate the counselor's empathic reactions. As an elementary assessment of the usability of the constructed multimodal empathy dataset, an interrater reliability analysis of annotators' subjective evaluations for video clips is conducted using the intraclass correlation coefficient and Fleiss' Kappa. Results prove that our data annotation is reliable. Furthermore, we conduct empathy prediction using three typical methods, including the tensor fusion network, the sentimental words aware fusion network, and a simple concatenation model. The experimental results show that empathy can be well predicted on our dataset. Our dataset is available for research purposes.

Facial landmark detection and action unit (AU) recognition are two essential tasks in facial analysis. Previous works rarely consider the relationship between these complementary tasks. In this article, we introduce a novel multi-task dual learning framework to exploit the relationship between facial landmark detection and AU recognition while simultaneously addressing both tasks. When both tasks share middle-level features, common patterns can be exploited and middle- and high-level features can be used to perform facial landmark detection and AU recognition, respectively. In addition, a dual learning mechanism is designed to convert the predicted landmarks and AUs of the label space to the corresponding facial image of the image space, further exploring the strong correlations between the tasks. By jointly training the proposed method at both the feature and label levels, each task improves the other. Experiments on two benchmark databases demonstrate that the proposed method can leverage dependencies to boost the generalization of both tasks.

Facial action unit (AU) recognition is formulated as a supervised learning problem by recent works. However, the complex labeling process makes it challenging to provide AU annotations for large amounts of facial images. To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition. The representation encoder is trained using large amounts of facial images without AU annotations. AU labeling rules are summarized from FACS to design facial partition manners and determine correlations between facial regions. The method utilizes a backbone network to extract local facial area representations and a project head to map the representations into a low-dimensional latent space. In the latent space, a contrastive learning component leverages the inter-area difference to learn AU-related local representations while maintaining intra-area instance discrimination. Correlations between facial regions summarized from AU labeling rules are also explored to further learn representations using a predicting learning component. Evaluation on two benchmark databases demonstrates that the learned representation is powerful and data-efficient for AU recognition.

In delay-tolerant networks(DTN), the timeliness of the node’s social circle and encounter time between nodes have a different effect in designing router algorithms. Considering these factors, this paper proposes an improved spray and wait algorithm based on the node social tree (TNST). Specifically, we will first combine the node’s own attributes with social ability to calculate the node’s delivery capability value. Followed by it, we build and update the social node tree with the delivery capability for each node. Finally, we will predict the encounter time between nodes, which based on their motion information. The node will select the encounter node as the relay node if the message’s destination node is in the node social tree. Otherwise, the node whose social tree with higher propagation capacity will be selected as the relay node. Simulation results show that the TNST algorithm improves the delivery rate and reduces network overhead.

Current approaches for facial landmark tracking predict facial landmarks through either a single tracker or an ensemble of trackers. However, the conventional ensemble is not designed for facial landmark tracking and can not capture spatial and temporal patterns of facial landmarks efficiently. In this paper, we propose to extend the conventional stacking with an adversarial training strategy to better suit the facial landmark tracking task. Specifically, the meta learner attempts to distinguish the predictions from the base learners with the ground truths, while the base learners attempt to confuse the meta learner by predicting landmarks close to the ground truths. The adversary between the two levels of learners forces them to fully capture the inherent spatial and temporal patterns of facial landmarks. Moreover, to promote the diversity of different base trackers, we design two classification tasks at both the feature level and prediction level. Experimental results on the 300VW dataset and the TF dataset demonstrate the effectiveness of our method, and we achieve state-of-the-art performances on both datasets.

Delay-Tolerant Networks (DTNs), a supplementary means of communication network in extreme situations, have aroused wide attention from scholars. However, it is challenging to efficiently utilize DTNs since they have intermittent and high-latency characteristics. In the design of DTNs routing scheme, the selection of relay nodes takes on a great significance in efficient communication. However, existing research has either considered only one of the node features, or simply fused node attributes without fully using their potential correlations. If the above problems are not effectively solved, the propagation of messages between nodes will become blind, and a considerable number of caches will be occupied and wasted by invalid copies. To solve the above challenges, a novel routing, “Traffic Light Routing Based on Node State Awareness (TLRNSA)”, is proposed for efficient communication. To be specific, the node's own state, the environmental state, and the historical encounter state are synthesized. The traffic value of the node is obtained based on the adaptive weight adjustment mechanism. The node is divided into three traffic light states, including red, green, and yellow, in accordance with the traffic value. Different routing strategies are developed for the above three states to enhance their performance. The results of the comprehensive experiments suggested that TLRNSA outperforms other state-of-the-art algorithms in delivery rate and latency. Compared with the two classic algorithms and the two optimized algorithms, the proposed method increases the delivery rate by 109.1%, 84.12%, 5.09%, and 1.09%, respectively, it reduces the delay by 32.16%, 36.46%, 32.77%, and 6.77%, respectively.

Vehicular Cloud and autonomous vehicles require a scalable and reliable mobile communication network. LTE and Dedicated Short Range Communication (DSRC) have been trying to fit for such role, yet neither can satisfy all requirement due to inherent architectural limitations. Fortunately the fifth generation mobile network, 5G and Mobile Edge Cloud/Computing (MEC) is around the corner, targeting ultra low packet delay, high reliability, and Gigabit level wireless bandwidth. This paper introduces a unique vehicular MEC architecture where instead of simply off-loading application service to the edge servers on MEC, vehicular communication packets are routed through the MEC network. We discuss in detail how it accommodates vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication with high scalability and guaranteed low packet delay. We also provide an in depth analysis of the pros and cons of our MEC vehicle network design, and address the mobility management issue on edge cloud. Applying distributed mobility management (DMM) operations we are able to make edge cloud IP handoff seamless and transparent. Proof of concept simulations are conducted using NS3.

Using directional antennas to reduce interference and improve throughput in multihop wireless networks has attracted much attention from the research community in recent years. In this paper, we consider the issue of minimum delay broadcast in multirate wireless mesh networks using directional antennas. We are given a set of mesh routers equipped with directional antennas, one of which is the gateway node and the source of the broadcast. Our objective is to minimize the total transmission delay for all the other nodes to receive a broadcast packet from the source, by determining the set of relay nodes and computing the number and orientations of beams formed by each relay node. We propose a heuristic solution with two steps. Firstly, we construct a broadcast routing tree by defining a new routing metric to select the relay nodes and compute the optimal antenna beams for each relay node. Then, we use a greedy method to make scheduling of concurrent transmissions without causing beam interference. Extensive simulations have demonstrated that our proposed method can reduce the broadcast delay significantly compared with the methods using omnidirectional antennas and single-rate transmission. In addition, the results also show that our method performs better than the method with fixed antenna beams. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we address the problem of broadcast routing and scheduling of video streaming for delay-sensitive applications in backbone WMNs. Given a source node and a set of destinations, our task is to build a broadcast routing tree and compute an optimal transmission schedule such that the network throughput for the source to broadcast streaming data to all the destinations is maximized. The problem is challenged by both rate-adaptive broadcast and inter/intra-flow interference caused by video streaming. We divide the whole period for video broadcast into identical time frames and prove that maximizing the total throughput can be converted into minimizing the length of a time frame. We formulate the minimization problem as a mixed integer quadratically constrained program and propose a three-step method as a solution. Firstly, we build the broadcast routing tree by defining a new routing metric to select the relay nodes. Then we use local search to adjust the tree structure. Last, we propose a greedy method to make scheduling of concurrent transmissions without causing inter/intra-flow interference. Extensive simulations have demonstrated that our proposed method can improve the performance significantly compared with existing routing and scheduling methods.

In this paper, we address the problem of joint power control and scheduling for minimizing broadcast delay in wireless mesh networks. Given a set of mesh routers and a routing tree, we aim to assign power for relay nodes and compute an optimal transmission schedule such that the total delay for a packet broadcast from the root to all the routers is minimized. We consider rate adaptation in our scheme. This is a difficult issue. High power enables high data rate but causes high interference, whereas low power allows more concurrent transmissions at the expense of data rate. We study the tradeoff between data rate and concurrency and propose a balanced method. We introduce a metric called standard deviation of average remaining broadcast time to determine the priority of the two parameters. When this metric is greater than a threshold, the nodes will take the data-rate-first approach to increase the data rate; otherwise, the concurrency-first approach will be used to increase the number of concurrent transmissions. Theoretical analysis is given to show the upper and lower bounds of this metric. Simulations have demonstrated that our proposed method can reduce the broadcast delay significantly as compared with existing methods.

In this paper, we address the problem of joint power control and scheduling for minimizing broadcast delay in wireless mesh networks. Given a set of mesh routers and a routing tree rooted from the gateway node, our task is to assign power for each relay node and compute an optimal transmission schedule such that the longest delay for a packet broadcast from the root node to all the other routers is minimized. We consider rate adaption in our scheme. This is a difficult issue. On one hand, if we increase the transmission power, the packet can be transmitted out at a higher data rate, which leads to less delay; on the other hand, a high transmission power would have larger interference range, which makes less nodes that can transmit concurrently and thus cause longer delay to deliver the packet to farther routers. We study the tradeoff between the two parameters, data rate and concurrency, and propose a balanced method for power control and transmission scheduling. We introduce a metric called standard deviation of remaining broadcast time of nodes to determine the priority of the two parameters. When this standard deviation is above a threshold, the transmitting nodes will take the data-rate-first approach to increase the data rate; otherwise the concurrency-first approach will be used to increase the number of concurrent transmissions in the system. Extensive simulations have demonstrated that our proposed method can reduce the broadcast delay significantly compared with the methods using fixed transmission power. In addition, the results also show that our balanced method performs better than both pure data-rate-first method and concurrency-first method.

In this paper, we study the data broadcast issue in wireless networks with directional antennas. We are given an Access Point(AP) and a set of clients served by the AP. Our task is to schedule the transmission of AP by using directional antennas such that the total delay for all clients to receive a broadcast packet is minimized. Directional antennas allow transmission energy to be concentrated on a narrow range along a direction, which can significantly increase the data rate of clients. However, there is a difficult issue in using directional antennas for data broadcasting. If the AP transmits the signals through a single lobe, the data rate of the clients covered by this lobe can be high but it takes more number of transmissions to cover all clients. On the other hand, if the AP transmits the signals through multiple lobes, it takes less number of transmissions for all clients to receive the data, but the signal strength decreases and the data rate drops. We study this tradeoff between the number of transmissions and the data rate. We prove that the data broadcasting problem is NP-hard and we propose a two-phase method to solve the problem. In the first phase, we use singlelobes to cover all clients such that the total transmission delay is minimized. In the second phase, we group these single-lobes into a set of multi-lobes. The goal is again to minimize the total transmission delay. The solution for each subproblem is optimal. Simulation results show significant improvements of performance compared with state of the art algorithms.

In this paper, we study the problem of enhancing the fault tolerance of IEEE 802.11 wireless local area networks in the design stage. Our goal is to place minimal number of APs (access points), such that the system can tolerate AP failures while guaranteeing QoS requirement. Given a set of clients (end-users) in a region, each client has a traffic demand for Internet access. Our concern is to find out minimal number of APs and their locations, so that both fault tolerance and QoS constraints can be satisfied. That is, when there is no AP failure, all clients' traffic demands should be met. When one AP fails, the clients it serves shall switch to other APs and a certain percentage of traffic demands of these clients affected shall still be met. We proposed a heuristic algorithm to solve this problem. Firstly we place minimal number of APs to ensure that connection requirements and per-client degrading traffic demand can be met. Secondly, we continue to add APs to meet per-client normal traffic demand. Finally, we place more APs to tolerate failure of the APs placed in the first step. Simulation are conducted to show the performance of our proposed method.

We study the real-time data aggregation in contention-based wireless sensor networks that use CSMA/CA MAC layer protocols as defined in IEEE 802.15.4 or IEEE 802.11 standard. The problem is, for a given data aggregation tree and a delay bound, to maximize the overall transmission success probability of all sensor nodes within the delay bound. In CSMA/CA protocols, the success probability and the expected transmission delay are highly sensitive to node interference, while the node interference is often very high in the large scale sensor networks. We propose a hybrid method that combines the CSMA/CA protocol with TDMA scheduling of transmissions. We divide the child nodes of a parent into groups and schedule the groups into different "time-frames" for transmission. Within the group, the nodes still use the CSMA/CA protocol to compete for data transmission. By doing so, we divide a large collision domain (i.e., all child nodes competing to transmit to their parent) into several small collision domains (i.e., a group of nodes competing for transmission), and the success probability can thus be significantly improved. On the other hand, the "time-frame" used in our method is much larger than the timeslot used in pure TDMA protocols. It only requires loose synchronization of clocks, which is suitable for low-cost sensor networks. We transform our objective of maximizing the overall success probability into minimizing the overall node interference. We then convert our problem to the maximum weight k-cut problem, which is NP-hard. We propose two efficient heuristic algorithms to solve the problem. Simulation results have shown that our proposed method can improve the success probability significantly compared with the method that uses pure CSMA/CA protocols.