Convolutional neural networks (CNNs) often exhibit overfitting due to overconfident predictions, which limits the effective utilization of training samples. Inspired by the diverse effects of training from different samples, we propose selective output smoothing regularization(SOSR) that improves model performance by encouraging the generation of equal logits on incorrect classes when handling samples that are correctly and overconfidently classified. This plug-and-play approach integrates seamlessly into diverse CNN architectures without altering their core design. SOSR demonstrates consistent improvements on various benchmarks, such as a 1.1% accuracy gain on ImageNet with ResNet-50 (77.30%). It synergizes effectively with several widely used techniques, such as CutMix and label smoothing, achieving incremental benefits, highlighting its potential as a foundational tool in advancing deep learning applications. Overall, SOSR effectively alleviates underutilization of high-confidence samples, enhances the generalizability of CNNs, and emerges as a robust tool for improving deep learning applications.

With an increasing number of sensor devices connected to industrial systems, the efficient and reliable aggregation of sensor data has become a key topic in Industrial Internet of Things (IIoT). Computing First Networks (CFN) are emerging as a promising technology for aggregating vast quantities of IIoT data. However, existing CFN data collection frameworks are usually centralized, which overly rely on third-party trusted authorities and fail to fully schedule and utilize limited computing resources. More critically, that is prone to trust and security issues. In this paper, considering the heterogeneity and data security in complex industrial scenarios, we propose a blockchain-based and multi-edge CFN collaborative IIoT data hierarchical collection framework (ME-CIDC) to collect massive IIoT data securely and efficiently. In ME-CIDC, a blockchain-driven resource allocation algorithm is proposed for inter-domain CFN, which achieves distributed and efficient task scheduling and data collection by constructing multiple blockchains. A self-incentive mechanism is designed to encourage inter-domain nodes to contribute resources and support the operation of the inter-domain CFN. We also propose an efficient double-layered data aggregation algorithm, which distributes computational tasks across two layers to ensure the efficient collection and aggregation of IIoT data. Extensive simulation and numerical results demonstrate the effectiveness of our proposed scheme.

Streaming triangle counting is a critical issue in graph stream mining, with applications in dense subgraph discovery, web mining, anomaly detection, and more. Recent efforts have focused on estimating triangle counts in graph streams, primarily through sampling methods. However, because of limited memory resources for handling high speed streams, traditional sampling methods suffer from reduced sampling rate and thereby performance loss. In this paper, we propose a new compact data structure called uHLL to process edge streams by considering the tradeoff between estimation accuracy and memory efficiency. Furthermore, different from conventional triangle counting algorithms, we solve the estimation of union set cardinality for edge-local triangle count under both centralized and distributed framework, so as to efficiently estimate the global triangle count by a one-pass streaming algorithm. To the best of our knowledge, this is the first implementation of a distributed framework using a compact data structure for streaming triangle counting. We provide theoretical proof of unbiasedness and derive the variance of the union set and global triangle count. We compare our scheme with 11 algorithms, showing that under the same experimental setting, uHLL and distributed uHLL are at least$ 2.3$and$ 1.7$times more accurate than the state-of-the-art, respectively.

In recent years, various loss functions have been proposed to boost the performance of deep neural networks. Every loss function has its own specific theoretical motivation, and can easily learn its preference features of training data compared with other loss functions. Thus, combining multiple loss functions to capture more data features becomes an attractive idea for model performance improvement. In this paper, instead of using a single loss function or a linear weighted sum of multiple loss functions, we present the method named Multiple Independent Losses Scheduling (MILS), which allows multiple loss functions to independently participate in the training process according to their performance. Specifically, for all candidate loss functions, one loss function will be predefined as the primary loss function before training, and the other loss functions will play auxiliary roles for possible contributions to improve the model performance. In order to avoid auxiliary loss functions bringing a negative effect on the model performance in the training process, we developed a simple but effective performance-based scheduling algorithm to prevent auxiliary loss functions from dragging down the model performance. Extensive experiments using various deep architectures on various recognition benchmarks demonstrate our scheme is simple, robust, lightweight, and effective for typical classification tasks.

Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. In contrast, multiple discriminators with different cost functions can yield various gradients for the generator, which indicates we can use them to search for more transportation maps in the latent space. Inspired by this, we have proposed a framework to combat the mode collapse problem, containing multiple discriminators with different cost functions, named CES-GAN. Unfortunately, it may also lead to the generator being hard to train because the performance between discriminators is unbalanced, according to the Cannikin Law. Thus, a gradient selecting mechanism is also proposed to pick up proper gradients. We provide mathematical statements to prove our assumptions and conduct extensive experiments to verify the performance. The results show that CES-GAN is lightweight and more effective for fighting against the mode collapse problem than similar works.

In this paper, we propose a simple yet effective data augmentation strategy, dubbed CyclicShift, to enrich data patterns. The idea is to shift the image in a certain direction and then circularly refill the resultant out-of-frame part to the other side. Compared with previous related methods, Translation, and Shuffle, our proposed method is able to avoid losing pixels of the original image and preserve its semantic information as much as possible. Visually and emprically, we show that our method indeed brings new data patterns and thereby improves the generalization ability as well as the performance of models. Extensive experiments demonstrate our method's effectiveness in image classification and fine-grained recognition over multiple datasets and various network architectures. Furthermore, our method can also be superimposed on other data augmentation methods in a very simple way. CyclicMix, the simultaneous use of CyclicShift and CutMix, hits a new high in most cases. Our code is open-source and available at https://github.com/dejavunHui/CyclicShift.

A novel coronavirus disease (COVID-19) is a pandemic disease has caused 4 million deaths and more than 200 million infections worldwide (as of August 4, 2021). Rapid and accurate diagnosis of COVID-19 infection is critical to controlling the spread of the epidemic. In order to quickly and efficiently detect COVID-19 and reduce the threat of COVID-19 to human survival, we have firstly proposed a detection framework based on reinforcement learning for COVID-19 diagnosis, which constructs a mixed loss function that can integrate the advantages of multiple loss functions. This paper uses the accuracy of the validation set as the reward value, and obtains the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have proposed a prediction framework that integrates multiple detection frameworks using parameter sharing to predict the progression of patients' disease without additional training. This paper also constructed a higher-quality version of the CT image dataset containing 247 cases screened by professional physicians, and obtained more excellent results on this dataset. Meanwhile, we used the other two COVID-19 datasets as external verifications, and still achieved a high accuracy rate without additional training. Finally, the experimental results show that our classification accuracy can reach 98.31%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 98.82%, 97.99%, 98.67%, and 0.989, respectively. The accuracy of external verification can reach 93.34% and 91.05%. What's more, the accuracy of our prediction framework is 91.54%. A large number of experiments demonstrate that our proposed method is effective and robust for COVID-19 detection and prediction.

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.

Mobile Edge Caching (MEC) can cache content at the edge of the network to reduce the delay and overhead of content transmission, which has become an effective method to solve the explosive growth of network traffic. To make good use of the limited resources in edge devices, many contents caching strategies use various methods to predict the popularity of content. However, caches get close to the edge of the network can lead to the rapid increase of caches' number and the user's requests are dispersed into a large number of caches, which leads to the popularity distribution of contents in edge caches is quite different and the number of first miss requests (the corresponding content is requested for the first time and is not in the cache) in edge caches becoming an essential factor affecting the cache hit rate. This paper first demonstrates the significant impact of the first miss requests through dataset analysis and establishes a mathematical model for the first miss problem in the edge cache. Then we analyze the similarity of requests received by caches and propose a proactive push algorithm based on similarity to improve the hit rate of edge caches. Through the trace-driven simulation experiment, we verify that the methods proposed in this paper can significantly improve the caches' hit rate.

Pedestrian trajectory prediction is a critical yet challenging task especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size. In this work, we propose a novel method, AVGCN, for trajectory prediction utilizing graph convolutional networks (GCN) based on human attention (A denotes attention, V denotes visual field constraints). First, we train an attention network that estimates the importance of neighboring pedestrians, using gaze data collected as subjects perform a bird’s eye view crowd navigation task. Then, we incorporate the learned attention weights modulated by constraints on the pedestrian’s visual field into a trajectory prediction network that uses a GCN to aggregate information from neighbors efficiently. AVGCN also considers the stochastic nature of pedestrian trajectories by taking advantage of variational trajectory prediction. Our approach achieves state-of-the-art performance on several trajectory prediction benchmarks, and the lowest average prediction error over all considered benchmarks.

With the rapid development of the Internet of Things (IoT), the continuous emergence of cyberattacks have brought great threat to the security of the network. Intrusion Detection System (IDS) which can identify malicious network attacks has become a strong tool to ensure network security. Many deep learning-based approaches have been used in IDS. However, most of these researches ignore the internal structural characteristics of the network traffic, and cannot accurately learn the key features of the malicious traffic. Thus, they have a low accuracy in classifying different kinds of network attacks. In this paper, we build an intrusion detection model DAL (Dense-Attention-LSTM, DAL), in which dense dilated convolutions is used to extract the underlying features of the network traffic. Then, attention mechanism is utilized to capture key features which represent the structural characteristics of traffic data. Moreover, CuDNN-based long short-term memory network is used to learn time-related information of the traffic while accelerating the convergence of the model. Finally, global maxpooling is adopted to compress data and to improve the generalization capabilities of the proposed model. Experimental results on UNSW-NB15 dataset show that the binary classification accuracy of the proposed model is up to 92.65%. Further, it can also identify various attacks with the accuracy of 81.28%. The performance of our model is better than some competing machine learning methods and some deep learning methods. We published our code at https://github.co-m/cKiNg37/IDS-model-DAL.

In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an image to a certain size and replace the random region of the original image with the reduced image. The generated image not only retains most of the original image information but also has global information in the reduced image. We call the reduced image as thumbnail. Furthermore, we find that the idea of thumbnail can be perfectly integrated with Mixed Sample Data Augmentation, so we put one image's thumbnail on another image while the ground truth labels are also mixed, making great achievements on various computer vision tasks. Extensive experiments show that Cut-Thumbnail works better than state-of-the-art augmentation strategies across classification, fine-grained image classification, and object detection. On ImageNet classification, ResNet-50 architecture with our method achieves 79.21% accuracy, which is more than 2.8% improvement on the baseline.

Vehicular edge computing has been a promising paradigm to offer low-latency and high reliability vehicular services for users. Nevertheless, for compute-intensive vehicle applications, most previous researches cannot perform them efficiently due to both the inadequate of infrastructure construction and the computing resource bottleneck of the edge server. Motivated by the fact that there is a large number of outside parked vehicles with rich and underutilized resources in the urban area, we propose the idea of parking edge computing, which makes use of the parked vehicles to assist edge servers in offloaded task handling. Specifically, on-street and off-street parked vehicles are first organized into parking clusters to act as virtual edge servers, participating in offloaded tasks execution in our framework. Second, a novel task scheduling algorithm is designed to jointly decide edge server selection and resource assignment. Furthermore, a local task scheduling policy is proposed as well, which reasonably allocates parked vehicles to perform the tasks with the aim of further improving task offloading performance. Finally, a time-related trajectory prediction model based on the random forest model is built, which helps to send back output result accurately. Our framework not only requires no additional infrastructure investment but also provides adequate computing resources. Simulation results based on a real city map and realistic traffic situations demonstrate that our framework provides more efficient and stable offloading services, especially in a large number of task requests condition.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as Attractive Route (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized Trip Recommender with POIs and Attractive Route (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.

With the breakthrough of deep learning, lip reading technologies are under extraordinarily rapid progress. It is well-known that Chinese is the most widely spoken language in the world. Unlike alphabetic languages, it involves more than 1,000 pronunciations as Pinyin, and nearly 90,000 pictographic characters as Hanzi, which makes lip reading of Chinese very challenging. In this paper, we implement visual-only Chinese lip reading of unconstrained sentences in a two-step end-to-end architecture (LipCH-Net), in which two deep neural network models are employed to perform the recognition of Pictureto-Pinyin (mouth motion pictures to pronunciations) and the recognition of Pinyin-to-Hanzi (pronunciations to texts) respectively, before having a jointly optimization to improve the overall performance. In addition, two modules in the Pinyin-to-Hanzi model are pre-trained separately with large auxiliary data in advance of sequence-to-sequence training to make the best of long sequence matches for avoiding ambiguity. We collect 6-month daily news broadcasts from China Central Television (CCTV) website, and semi-automatically label them into a 20.95 GB dataset with 20,495 natural Chinese sentences. When trained on the CCTV dataset, the LipCH-Net model outperforms the performance of all stateof-the-art lip reading frameworks. According to the results, our scheme not only accelerates training and reduces overfitting, but also overcomes syntactic ambiguity of Chinese which provides a baseline for future relevant work.

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, one-pass streaming algorithm for counting global and local triangles has been widely studied, and most researches focus on the single-machine streaming algorithm in a 'offline+batch processing' mode. However, researches on distributed online algorithm on multiple machines are still in its infancy, and this stage has not been thoroughly studied. In this paper, we investigate the triangle counting problem in large-scale simple undirected graphs whose edges arrive as a stream. We propose two distributed online streaming algorithms to estimate the global number of triangles, which are based on the current best performance sampling-based streaming algorithm. We mainly realize the reasonable partition of the graph stream, so that each worker independently estimates the number of triangles in a subgraph of the graph stream. Experimental results show that our algorithms reduce the estimation error and are several times more accurate than state-of-the-art streaming algorithms.

Pedestrian flow is an important indicator of public places, since it can provide more potential economic benefits. Pedestrian flow prediction is developed to help the decisionmaking for the operators (such as shopping center owner). Furthermore, the operators aperiodically arrange some events to attract the nearby pedestrians, such as the sales promotions, and we term this kind of events as business event. Moreover, their placement will affect the distributions of the pedestrian flows. In this paper, we investigate the influence of the business events on the pedestrian flows. Then, we propose an Attraction Based Matrix Factorization model, called ABMF, to efficiently predict the pedestrian flow with business events and enable operators to formulate candidate solutions. The experimental results show the superiority of our prediction method compared with other state-of-the-art prediction techniques.

To improve the experience of various network applications, dynamic rate allocation is an essential issue in recent ISP networks. The emergence of software-defined networking (SDN) and the OpenFlow specification makes dynamic rate allocation in ISP networks efficient. The allocation could locally run on a home network gateway (edge switch) under SDN, but such local range of rate allocation reduces the overall fairness and performance in the whole network. However, under a global range, the request of rate allocation from a small number of hosts will cause all switches on the entire network to participate. This is termed as cascading problem, which causes a high cost for re-allocating the rates with the global range of switches. In this paper, we investigate the cascading problem for dynamic rate allocation with SDN, and discuss the tradeoff between the performance and cost for the range of rate allocation. We propose a Rate Allocation algorithm with Limited Range (RALR) in SDN, and discuss it for dynamic rate allocation by the theory of Lyapunov drift. Our intensive simulations verify the performance of the proposed strategy of rate allocation in SDN.

Software Defined Networks (SDN) have been proposed for use in applications of the Internet of Things (IoT), termed as software-defined IoT (SD-IoT) network, because of the popularity and capability of mobile devices being used for networking in relatively large areas. However, a single controller in SDN has a limited request-processing capability, so a distributed control plane with multiple physical controllers has been used to achieve scalability and reliability for supporting the IoT applications. Accordingly, the data plane of an SDN is partitioned into multiple domains, and each controller just takes over one. When considering both delays and loads of requests to the controllers, a partitioning problem arises. It is required to consider the distributions of flow paths, since inter-domain flow paths will create an extra load of requests to the controllers. In this paper, we investigate the partitioning problem in SD-IoT networks. Since uploading sensing data through the IoT gateways are non-uniform, we utilize a hypergraph to model the relationship between the spatial events and the gateways in IoT for urban sensing. We propose a Partitioning Algorithm for Software-defined IoT Network (PASIN) to partition the SDN by considering both delays and loads of requests from the flow paths. Our extensional simulations verify the effectiveness of our proposed approach.

Recent breakthrough progress of wireless energy transfer technology and rechargeable lithium battery technology emerge the wireless rechargeable sensor networks(WRSNs). In WRSNs, how to schedule the mobile charger to efficiently replenish energy for sensor nodes is very challenging. However, most of current existing WRSNs mobile energy replenishment schemes either cannot adapt to the dynamic and diversity energy consumption of sensors in actual environment or leave out of consideration of the fairness of charging response, which may result in sensor nodes failure due to energy depletion and low charging performance. Particularly, the nodes failure issue will get worse when there are a large number of charging requirements in the network. In this paper, we explore the node energy depletion problem in mobile charging for WRSNs and propose a node failure avoidance online charging scheme(NFAOC). To avoid the nodes failure due to energy depletion, NFAOC compares the current maximum tolerable charging delay of each request node with its shortest waiting time for charging, and then always chooses the nodes which make the least number of other request nodes suffer from energy depletion as the charging candidates. Simulation results demonstrate that NFAOC can effectively solve the node energy depletion problem with lower charging latency and charging cost in comparison with other current existing online charging schemes.

Software Defined Networks (SDN) have been utilized in applications of the Internet of Things (IoT), termed as software-defined IoT network, because of the popularity and capability of mobile devices being used for networking in relatively large areas. In a software-defined IoT system, the sensing data are asynchronously harvested by the mobile sensing nodes, and are also asynchronously uploaded to the gateways of a software-defined network. Thus, all the sensing data are asynchronously transmitted from the gateways to the data servers in the pattern of multipoints-to-point (M2P) data transmissions. Even if the sensing data are generated from the same sensing event, the controller of SDN has no knowledge about this relationship. Thus, such asynchronous M2P data transmissions from the same sensing event at the gateways will generate many redundant requests to their controller by OpenFlow protocol of SDN. In this paper, we investigate the redundant requests caused by the asynchronous M2P data transmissions in the software-defined IoT network. We model the relationship between the sensing events and the uploading gateways by utilizing their spatial locations and the distribution of mobile sensor nodes. To reduce the loads on the controller for the asynchronous M2P data transmissions, we propose an One-Request Scheme for Software-Defined IoT Networks (ORSIN), to batch the updating the forwarding rules of the multiple data transmissions from the same event by the first one request from a gateway. Our extensional simulations verify the effectiveness of our proposed approach.

We present here a fundamental understanding of the capacity of wireless ad hoc networks. Under the assumption that all interference is essentially regarded as noise, we try to answer the question: “What is the lower bound on network capacity under a certain power assignment and nodal distribution?” We give two quantitative lower bounds: one is measured in bps (bits per second), the other is measured in bmps (bit-meters per second). We also investigate the limit of the lower bounds as well as their tightness. The results of this paper may be worth considering by wireless network designers in hostile environments.

With the development of Location Based Services (LBSs), both academic researchers and industries have paid more attention to GPS-less mobile localization on mobile phones. The majority of the existing localization approaches have utilized signal-fingerprint as a metric for location determinations. However, one of the most challenging issues is the problem of uncertain fingerprints for building the fingerprint map, termed as the jigsaw puzzle problem. In this paper, for more accurate fingerprints of the mobile localization, we investigate the changes of Received Signal Strength Indication (RSSI) from the connected cell-towers over time along the mobile users' trajectories, termed as RSSI Time Series (RTS). Thus, we propose an RTS Assisted Localization System (RALS), which is a GPS-less outdoor mobile localization system. For localization, an RTS map is built on the back-end server, which consists of RTS harvested from the mobile phones, by the way of crowd sensing. The jigsaw puzzle problem slows down the map construction solely by the regular unintentional users with short-distance trajectories, and affects its efficiency. To speed up the map construction, we propose employing a few advanced intentional users with additional long-distance trajectories, at a higher cost than the regular user, this is called extra mile. Our extensional experiments verify the effectiveness of our localization system.

The development of the smartphones with various sensors, and powerful capabilities (computing, storage, and communication), motivates a popular computing and sensing paradigm, crowdsensing. In general, in crowdsensing, the smart-phones sense and collect the sensory data from a large number of smartphone users, for distinguishing the uncertain objects. However, some existing solutions for crowdsensing usually prefer to utilize only one or few features to distinguish the uncertain objects. In this paper, due to the limitation of less features, we propose to utilize multiple features to distinguish the uncertain objects for crowdsensing. For distinguishing uncertain objects with multiple features, we propose to utilize KL divergence based clustering. Moreover, we introduce two other mutated forms, the symmetry KL divergence and Jensen-Shannon KL divergence, to improve our algorithm. We evaluate our proposed schemes with real data of multiple features, which are collected by the smartphones with the sensors.

The data delivery in Vehicular Ad Hoc Networks (VANETs) depends on the mobility of the vehicles (e.g. with carry-and-forward). However, the mobility of the vehicles is not only affected by the nodes themselves, but also by some external means such as the traffic lights. The red light stops the vehicles at the intersection, which will increase the delivery delay of the messages carried by the vehicle with waiting time. On the contrary, this may also increase the opportunities of vehicles moving behind to catch up in forwarding messages. In this paper, we investigate the negative and positive influences of the traffic lights on data delivery in VANETs. We develop an analysis model for evaluating the data delivery among the vehicles that move along a path with multiple traffic lights. Based on the model, vehicles can estimate the reachability of destinations and the data delivery delay. Thus, we propose a transmission control scheme by the given deadline of reachable destinations, in order to improve the data delivery. Our intensive simulations verify the proposed model, and evaluate the influence of the traffic lights on data delivery.

Uncertain data are inherent in mobile crowd sensing applications, and the objects that they correspond to are usually vaguely specified. In order to improve performance, we often increase the number of features. However, the more features are used, the more redundancy and cost are involved correspondingly. Therefore, the number of features we selected for a specified application is a tradeoffs between the accuracy and the cost. In this paper, we model such tradeoffs between accuracy and cost as an optimization problem. Moreover, for investigating this problem, we propose to model the sensing with multiple features under a hypercube structure. In our scheme, each feature of uncertain objects is represented as a component of the vertex's coordinate in hypercube. At the same time, we prefer to define the edges between vertices with relative entropy rather than Euclidean distance. Because the former one could accurately measures the difference between two probability distributions of data. We evaluate our proposed schemes with real data of a crowd sensing recognition case, which are collected by smartphones with sensors.

To facilitate the safe and comfortable driving, vehicular ad hoc networks (VANETs) will be flooded with plenty of multimedia files, such as images, music and video clips. However, due to the dynamic and transient contacts between moving vehicles, these multimedia files distribution over VANETs often involves transmission failure and terrible user experience. In this paper, we propose an efficient infrastructure-less multimedia publish/subscribe scheme for an urban area. In cities, there are lots of parked vehicles, presenting as parking clusters, owning the ability of calculation, storage and communication. Our scheme relies on these parking clusters to cache and distribute the multimedia files for moving users. For each subscription, the parking cluster distributes the file chunks to the subscriber during their contact time. For the remained content chunks, the parking cluster will distribute them to slave vehicles that have no downloading request. Then, the slave vehicles transfer the received file chunks to a parking cluster, where the subscriber can continue the unfinished downloading when it drives through. Theoretical results illustrate the effectiveness of our approach and extensive simulations results demonstrate that the proposed scheme achieves a higher downloading ratio with different file sizes, especially in sparse traffic and multiple subscribers conditions.

Data delivery in Vehicular Ad Hoc Networks (VANETs) is based on the vehicles on the roads. However, the distribution of vehicles could be affected by some external means. For example, the traffic light or pedestrian signal could block the traffic flow moving onto a road. Thus, a gap between vehicles will appear at the entrance of the road, where the distance is larger than the communication range of the vehicles. We term it as a traffic hole, which not only affects the forwarding opportunities in VANETs, but also affects the performance of data delivery on the road, even under heavy traffic. In this paper, we model and analyze the traffic hole problem to characterize the pattern of traffic holes in VANETs. Then we discuss its influence on the data delivery in VANETs, and propose to utilize the backward traffic to mitigate the traffic hole problem. We conduct intensive simulations for discussing the traffic hole problem in VANETs. The simulation results imply that signal operations can affect the performance of data delivery in VANETs, and suggest that the backward traffic can mitigate the traffic hole problem.

The emergence of cloud computing has promoted growing demand and rapid deployment of data centers. However, data center operations require a set of sophisticated skills (e.g., command-line-interface), resulting in a high operational cost. In this demo, to reduce the data center operational cost, we design and build a novel cloud data center management system, based on the concept of 3D gamification. In particular, we apply data visualization techniques to overlay operational status upon a data center 3D model, allowing the operators to monitor the real-time situation and control the data center from a friendly user interface. This demo highlights: (1)a data center 3D view from a First Person Shooter (FPS) camera, (2)a run-time presentation of visualized infrastructures information. Moreover, to improve the user experience, we employ cutting-edge HCI technologies from multi-touch, for remote access to Cloud3DView.

In Vehicular Ad Hoc Networks (VANETs), content distribution directly relies on the fleeting and dynamic contacts between moving vehicles, which often leads to prolonged downloading delay and terrible user experience. Deploying Wifi-based Access Points (APs) could relieve this problem, but it often requires a large amount of investment, especially at the city scale. In this paper, we propose the idea of ParkCast, which doesn't need investment, but leverages roadside parking to distribute contents in urban VANETs. With wireless device and rechargable battery, parked vehicles can communicate with any vehicles driving through them. Owing to the extensive parking in cities, available resources and contact opportunities for sharing are largely increased. To each road, parked vehicles at roadside are grouped into a line cluster as far as possible, which is locally coordinated for node selection and data transmission. Such a collaborative design paradigm exploits the sequential contacts between moving vehicles and parked ones, implements sequential file transfer, reduces unnecessary messages and collisions, and then expedites content distribution greatly. We investigate ParkCast through theoretic analysis and realistic survey and simulation. The results prove that our scheme achieve high performance in distribution of contents with different sizes, especially in sparse traffic conditions.

Comfortable travel is an essential issue of Intelligent Transport Systems (ITS). However, the driver's behavior and the road condition affect the comfort of the passenger's riding experience while they are traveling. In this paper, we propose a system named Riding Experience Sensor (RESen) for sensing and evaluating the riding experience, based on crowd sourcing by smart phones. We utilize the acceleration sensor and gravity sensor for sensing with arbitrary orientations of smart phones. We partition the riding experience into horizontal and vertical for evaluation. Thus, based on the driver's historical trajectories, the system can provide feedbacks for improving driving by finding the anomalies along these trajectories. Based on the map, which has evaluated the comfort of each road, the system can provide a comfortable travel plan for query users.

Advertisement dissemination via vehicular ad hoc networks (VANETs), fuelled by commercial interests, has attracted tremendous research efforts lately. However, the existing ads dissemination schemes could either incur a high operational overhead in inter-vehicle communication or lead to a substantial capital overhead of constructing roadside infrastructure. To reduce the aforementioned costs, we propose IPAD (Intelligent Parking-assisted Ads Dissemination), which taps into the unused resources (e.g., wireless device, rechargeable battery, storage capability, on-board computer chip) offered by roadside parking in urban areas to facilitate ads dissemination to mobile vehicles. Our proposed IPAD scheme is substantiated with a novel architecture, which is cost saving and could support large scale ads dissemination. To realize efficient ads dissemination over this artitecure, we put forward an effective routing scheme to distribute each ad to appropriate roadside parking and introduce the pub/sub scheme into the last stage of ads dissemination. Finally, we investigate IPAD through theoretic analysis and simulation. The numerical results obtained verify that our scheme offers an enhanced ad delivery ratio and reduces the network traffic overhead in ads dissemination via VANETs.

Disasters (e.g., earthquakes, flooding, tornadoes, oil spilling and mining accidents) often result in tremendous cost to our society. Previously, wireless sensor networks (WSNs) have been proposed and deployed to provide information for decision making in post-disaster relief operations. The existing WSN solutions for post-disaster operations normally assume that the deployed sensor network can tolerate the damage caused by disasters and maintain its connectivity and coverage, even though a significant portion of nodes have been physically destroyed. In reality, however, this assumption is often invalid for disastrous events like earthquakes in large scale, limiting the relief capability of the existing solutions. Inspired by the “blackbox” technique in flight industry, we propose that preserving “the last snapshot” of the whole network and transferring those data to a safe zone would be the most logical approach to provide necessary information for rescuing lives and control damages. In this paper, we introduce Data Evacuation (DE), an original idea that takes advantage of the survival time of the WSN, i.e., the gap from the time when the disaster hits and the time when the WSN is paralyzed, to transmit critical data to sensor nodes in the safe area. Mathematically, the problem can be formulated as a nonlinear programming problem with multiple minimums in its support. We propose a gradient-based DE algorithm (GRAD-DE) to verify our DE strategy. Numerical investigations reveal the effectiveness of GRAD-DE algorithm.

In Vehicular Ad Hoc Networks (VANETs), the major communication challenge lies in very poor connectivity, which can be caused by sparse or unbalanced traffic. Deploying supporting infrastructure could relieve this problem, but it often requires a large amount of investment and elaborate design, especially at the city scale. In this paper, we propose the idea of Parked Vehicle Assistance (PVA), which allows parked vehicles to join VANETs as static nodes. With wireless device and rechargable battery, parked vehicles can easily communicate with one another and their moving counterparts. Owing to the extensive parking in cities, parked vehicles are natural roadside nodes characterized by large number, long-time staying, wide distribution, and specific location. So parked vehicles can serve as static backbone and service infrastructure to improve connectivity. We investigate network connectivity in PVA through theoretic analysis and realistic survey and simulations. The results prove that even a small proportion of PVA vehicles could overcome sparse or unbalanced traffic, and promote network connectivity greatly. Thus, PVA enhances VANETs from down to top, and paves the way for new hybrid networks with static and mobile nodes.

Vehicular ad hoc networks (VANETs) represent promising technologies for improving driving safety and efficiency. Due to the highly dynamic driving patterns of vehicles, it has been a challenging research problem to achieve effective and time-sensitive data forwarding in vehicular networks. In this paper, a Shared-Trajectory-based Data Forwarding Scheme (STDFS) is proposed, which utilizes shared vehicle trajectory information to address this problem. With access points sparsely deployed to disseminate vehicles' trajectory information, the encounters between vehicles can be predicted by the vehicle that has data to send, and an encounter graph is then constructed to aid packet forwarding. This paper focuses on the specific issues of STDFS such as encounter prediction, encounter graph construction, forwarding sequence optimization and the data forwarding process. Simulation results demonstrate the effectiveness of the proposed scheme.

Vehicular Ad Hoc Networks (VANETs) are getting increasing attention from academic researchers and automotive industries. Timely and cost-efficient multi-hop data delivery among vehicles is essential for VANETs, and various routing protocols are envisioned for infrastructure-less vehicle-to vehicle (V2V) communications. Due to the road-constrained data delivery and highly dynamic topology of vehicle nodes, it's better to construct routing based on the road-to-road pattern than the traditional node-to-node routing pattern in MANETs. However, the challenging issue for the road-to-road routing in VANETs is the opportunistic forwarding at intersections. Therefore, we propose a novel routing scheme, called Buffer and Switch (BAS). In BAS, each road buffers the data packets with multiple duplicates propagation in order to provide more opportunities for packet switching at intersections. Different from conventional protocols in VANETs, the propagation of duplicates in BAS is bidirectional along the routing path. Moreover, BAS's cost is much lower than other flooding-based protocols due to its spatio-temporally controlled duplicates propagation. We conduct the extensive simulations to evaluate the performance of BAS based on the road map of a real city collected from Google Earth. The simulation results show that BAS can outperform the existing protocols, especially when the network resources are limited.

Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of Vehicle-to-Passenger communication (V2P), which allows direct, instant, and flexible communication between moving vehicles and roadside passengers. With pocket wireless devices, passengers can easily join VANETs as roadside nodes, and express their travel demands, e.g., taking a free ride or calling a taxi via radio queries over VANETs. Once a matched vehicle is found through the disseminated queries, the driver can decide whether to provide corresponding services, especially the carrying of passengers and goods. We investigate the main challenges in vehicle calling, establish a trip history model to predict vehicle movement, and develop typical query dissemination schemes to match the target vehicle in vehicular networks. With V2P over VANETs, vehicle transportation is capable of open and efficient P2P information interaction, and thus benefits from relevant efficiency improvement. Based on a realistic travel survey and simulation, we prove that vehicle calling is effective and efficient in casual carpooling and taxi calling.

This paper presents a fundamental understanding regarding the effect that transmitting power has on the capacity of wireless ad hoc networks. Under the assumption that all interference is essentially regarded as noise, we carry out a quantitative analysis from the perspective of information theory. First, we answer the question, "How much information can be carried per unit bandwidth over a wireless ad hoc network under a certain power assignment and nodal distribution?" We then prove that the maximum network capacity, whether in bps (bits per second) or in bmps (bit-meters per second), strictly increases with respect to the total transmitting power under a fixed-proportion assignment, and that there is a limit as the total transmitting power goes to infinity. We further conclude that the maximum power efficiency, whether in bpJ (bits per Joule) or in bmpJ (bit-meters per Joule), strictly decreases with respect to the total transmitting power under a fixed-proportion assignment. We also show that the maximum network capacity, whether in bps or in bmps, follows an O(n) scaling law, where n is the number of nodes, which coincides with previous asymptotic conclusions. Finally, we highlight the practical implications of the results for power allocation, power assignment, and transmission scheduling. The contributions of this paper may be worthy of consideration by wireless network designers.

Power control and routing are two fundamental supporting techniques for wireless communications in ad hoc networks. However, most existing power control and routing proposals are not really efficient enough, due to separate considerations on them. In this paper, motivated by the observation on the necessity and feasibility of the combination of power control and routing, we propose a power controlled routing protocol PCAR (Power Controlled Ad hoc Routing). The basic idea is to develop power control on top of the distance-vector routing mechanism that is based on the classical distributed Bellman-Ford algorithm. Each node adjusts the transmission power automatically through the PIPC (Proportion-Integral Power Control) algorithm that we previously proposed. Both theoretical analysis and simulation results show that PCAR has a good performance.

The delay tolerant mobile sensor network (DTMSN) is a new type of sensor network for pervasive information gathering. Although similar to conventional sensor networks in hardware components, DTMSN owns some unique characteristics such as sensor mobility, intermittent connectivity, etc. Therefore, traditional data gathering methods can not be applied to DTMSN. In this paper, we propose an efficient motion tendency-based data delivery scheme (MTAD) tailored for DTMSN. By using sink broadcast instead of GPS, MTAD obtains the information about the nodal motion tendency with small overhead. The information can then be used to evaluate the node's effective delivery ability and provide guidance for message transmission. MTAD also employs the message survival time to effectively manage message queues. Our simulation results show that, compared with other DTMSN data delivering approaches, MTAD achieves not only a relatively longer network lifetime but also a higher message delivery ratio with lower transmission overhead and data delivery delay.

The basic operation of delay tolerant sensor network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, to extend a Pub/Sub system in DTSN has become a promising topic. However, due to the unique characteristic of frequent partitioning in DTSN, to extend a Pub/Sub system in DTSN is a considerably difficult and challenging problem, and there is no good solution to it in existing works. To adapt Pub/Sub systems to DTSN, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies.

One of the main objectives of query processing in wireless sensor networks is to achieve high resource efficiency. In this paper, we focus on scheduling multiple queries which may have temporal and spatial overlapping in querying operations. Our purpose is to schedule the operations on the sensor nodes so as to save the total cost by maximizing the overlapping in time among the queries. We propose an algorithm, called RSQS, which allows the newly arrived query to best share the operations with existing queries. The simulation results showed that our proposed approach can be used to effectively reduce the amount of sensory data transmitted in the network. This leads to energy and bandwidth saving in wireless sensor network.

The basic operation of delay tolerant mobile sensor network (DTMSN) is for pervasive data gathering in networks with intermittent connectivity, where traditional data gathering methods can not be applied. In this paper, an efficient mobility prediction-based adaptive data gathering protocol (MPAD) based on the random waypoint mobility model tailored for DTMSN is proposed. In MPAD, a node independently makes decision to replicate messages and send them to the neighbor sensor nodes with a higher probability of meeting the sink node. MPAD consists of two components for data transmission and queue management. Data transmission makes decisions on when and where to transmit data messages according to the node delivery probability, and the queue management employs the message survival time to decide whether the message should be transmitted or dropped for minimizing the transmission overhead. Simulation results show that the proposed MPAD achieves the longer network lifetime and the higher message delivery ratio with the lower transmission overhead and data delivery delay than some other previous solutions designed for DTMSN, such as direct transmission, flooding and message fault tolerance-based data delivery protocol (FAD).

Marking in DiffServ at the edge of the network often follows a demand side policy. It meters a traffic stream and marks its packets according to some predefined traffic parameters as Committed Information Rate, Committed Burst Size, Excess Burst Size and so on. Such marking based on traffic characteristics is irrespective to network dynamics, which causes collision and QoS degradation in DiffServ. This paper proposes flow-based Reservation Marking as a supply side marking at the network edge, which marks stream packets reserved or unreserved according to flow-specific reservation in a distributed resource reservation environment. When congestion occurs, anticipant per flow QoS is secured by protecting reserved packets on core routers without any per flow or per trunk management. It provides a simple, scalable and adaptive mechanism of implementing quantitative end-to-end QoS by mapping per flow in IntServ into per class in DiffServ. A "once reserve, no more manage" framework is constructed to eliminate flow state, avoiding unexpected collision and flow management simultaneously on core routers. Performance evaluation reveals that it affords controllable and quantitative QoS, keeps networks core-stateless and achieves high link utilization at the same time.

In a wireless sensor network (WSN), the energy hole problem is a key factor which affects the lifetime of the networks. In a WSN with circular multi-hop deployment (modeled as concentric coronas), sensors in one corona have the same transmission range termed as the transmission range of this corona,

Data gathering is a major function of many applications in wireless sensor networks (WSNs). The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirement of applications/users such as sensing area coverage. In this paper, we propose a novel hierarchical clustering protocol for long-lived sensor network. EAP achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all nodes. EAP introduces a new clustering parameter for cluster head election, which can better handle the heterogeneous energy capacities. Furthermore, it also introduces a simple but efficient approach, namely intra-cluster coverage to cope with the area coverage problem. We evaluate the performance of the proposed protocol using a simple temperature sensing application. Simulation results show that our protocol significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered.

Publish/subscribe (pub/sub) systems have been widely used in distributed computing systems for event notification and delivery. However, there is no existing work on pub/sub systems for wireless mesh networks (WMNs) which are regarded as a promising infrastructure for providing wireless Internet services to a wide area. In this paper, we propose the design of a pub/sub system for WMNs. First, we describe a pub/sub system model for WMNs to support mobile clients. Then, based on geographical routing and mobility prediction, we propose an event delivery protocol with low transmission overhead and delay to support mobile clients in a WMN. Our theoretical analysis shows that the transmission overhead and delay per event of the proposed protocol are only affected by the area of the region where the clients move, but not the speeds of mobile clients and the arrival rates of events. The analysis has been validated by the simulation results which show that our protocol can significantly improve the performance of event transmission in pub/sub systems for WMNs compared with the previous solutions designed for other kinds of networks.

This paper considers the problem of constructing data gathering trees in a wireless sensor network for a group of sensor nodes to send collected information to a single sink node. Sensors form application-directed groups and the sink node communicates with the group members, called source nodes, to gather the desired data using a multicast tree rooted at the sink node. The data gathering tree contains the sink node, all the source nodes, and some other non-source nodes. Our goal of constructing such a data gathering tree is to minimize the number of non-source nodes to be included in the tree so as to save energies of as many non-source nodes as possible. It can be shown that the optimization problem is NP-hard. We first propose an approximation algorithm with a performance ratio of four, and then give a distributed algorithm corresponding to the approximation algorithm. Extensive simulations are performed to study the performance of the proposed algorithm. The results show that the proposed algorithm can find a tree of a good approximation to the optimal tree and has a high degree of scalability.