Label correction methods are popular for their simple architecture in learning with noisy labels. However, they suffer severely from false label correction and achieve subpar performance compared with state-of-the-art methods. In this paper, we revisit the label correction methods through theoretical analysis of gradient scaling and demonstrate that the sample-wise dynamic and class-wise uniformity of interpolation weight prevents memorization of the mislabeled samples. We then propose DULC, a simple yet effective label correction method that uses the normalized Jensen-Shannon divergence (JSD) metric as the interpolation weight to promote sample-wise dynamic and class-wise uniformity. Additionally, we provide theoretical evidence that sharpening predictions in label correction facilitates the memorization of true class, and we achieve it by employing the augmentation strategy along with the sharpening function. Extensive experiments on CIFAR-10, CIFAR-100, TinyImageNet, WebVision and Clothing1M datasets demonstrate substantial improvements over state-of-the-art methods.

Adaptive optimizers such as Adam and RMSProp have gained attraction in complex neural networks, including generative adversarial networks (GANs) and Transformers, thanks to their stable performance and fast convergence compared to non-adaptive optimizers. A frequently overlooked limitation of adaptive optimizers is that adjusting the learning rate of each dimension individually would ignore the knowledge of the whole loss landscape, resulting in slow updates of parameters, invalidating the learning rate adjustment strategy and eventually leading to widespread insufficient convergence of parameters. In this paper, we propose HVAdam, a novel optimizer that associates all dimensions of the parameters to find a new parameter update direction, leading to a refined parameter update strategy for an increased convergence rate. We validated HVAdam in extensive experiments, showing its faster convergence, higher accuracy, and more stable performance on image classification, image generation, and natural language processing tasks. Particularly, HVAdam achieves a significant improvement on GANs compared with other state-of-the-art methods, especially in Wasserstein-GAN (WGAN) and its improved version with gradient penalty (WGAN-GP).

Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-dimensional state spaces. To address this limitation, we propose a novel approach, Efficient Diversity-based Experience Replay (EDER). EDER employs a determinantal point process to model the diversity between samples and prioritizes replay based on the diversity between samples. To further enhance learning efficiency, we incorporate Cholesky decomposition for handling large state spaces in realistic environments. Additionally, rejection sampling is applied to select samples with higher diversity, thereby improving overall learning efficacy. Extensive experiments are conducted on robotic manipulation tasks in MuJoCo, Atari games, and realistic indoor environments in Habitat. The results demonstrate that our approach not only significantly improves learning efficiency but also achieves superior performance in high-dimensional, realistic environments.

Recent advancements in hyperspectral images classification (HIC) rely on high-quality annotations and thus inevitably suffer from noisy labels. To address the negative effects of noisy labels, some methods employ neighborhood samples to select clean samples and demonstrate promising results. However, they typically rely on robust feature extraction and remain limited under high noise ratios. To overcome the limitations, we propose a novel robust sample selection and correction method based on robust contrastive learning and neighborhood feature modeling. The proposed RSC adopts a dual-branch spectral-spatial network combining spatial and channel-based residual attention modules to extract robust feature. Furthermore, unsupervised contrastive learning at both feature and logit-level are introduced to bolster the feature extractor. Finally, a clean sample selection strategy based on neighborhood consistency in feature space and relabelling scheme by the maximum confidence are integrated to resist the noisy labels. Extensive experiments conducted on publicly available hyperspectral datasets, including Houston and Indian Pines, demonstrate the superior performance of the proposed method, particularly in high noise ratios, where substantial improvements in classification accuracy are observed. The code is available at https://github.com/kovelxyz/RSC.

With the continued proliferation of wireless communication and mobile devices equipped with built-in GPS sensors, burgeoning applications of location-based services are springing up, such as mobile crowdsourcing applications (MCS), which are revolutionizing our daily lives. However, collecting and sharing continuously spatio-temporal data to a service provider of MCS applications will incur users' concerns about their privacy. In this paper, we study the problem of locally differentially private data aggregation over distributed spatial-temporal infinite streams. To this end, we proposed a LDP-based framework for dealing with the problem. Firstly, we propose a novel model of (w, ε)-Clustering-based Local Differential Privacy ((w, ε)-CLDP) to capture the temporal and spatial correlations in spatio-temporal infinite stream while guaranteeing stringent differential privacy. Secondly, we develop an efficient GRR-based Local Budget Absorption (LBA) mechanism as a building block for achieving (w, ε)-CLDP and present its privacy analysis. On this basis, we present a framework of real-time spatio-temporal data aggregation over distributed infinite streams with an untrusted server. Lastly, we conduct experiments on two real-world datasets to validate our framework. The results manifest that the LBA-based framework is optimal in data utility for real-time spatio-temporal data aggregation with a rigorous privacy guarantee.

The increasing interest in loss prevention devices using the Internet of Things, has been driven by the convenience, low cost, and low-power consumption of these devices. However, the existing technologies cannot achieve a balance between high availability over a wide area with a single smartphone and precise location awareness. In this article, a novel precise-location-aware method that integrates acoustic technology and pedestrian dead reckoning (PDR), ChirpTracker, is proposed which most smartphones support without auxiliary equipment. This system can provide wide coverage (30 m) and is suitable for many scenarios, such as searching for cars in underground parking or finding items indoors. ChirpTracker can detect the distance between the smartphone and a lost tag in real time using acoustic signals, it can monitor the relative position change of the smartphone based on deep learning-based PDR and update relative positioning of the lost tag though the observation from single base-station in motion. A technology that combines the local least squares method (LSM) and particle filter (PF) improves the convergence and the robustness of ChirpTracker through an identification strategy for a mirror position. This method was validated in experiments conducted in actual environments. The results demonstrate the effectiveness and positioning accuracy of ChirpTracker.

Home exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent need to prevent workout injuries and improve exercise performance. Recent studies with inertial measurement units (IMUs) or audio signals are neither computationally feasible for real-time motion tracking with satisfactory accuracy using COTS devices nor practically usable due to the interference with noisy ambient environments. In this paper, we propose UltraMotion, a real-time, high-precision ultrasonic arm motion tracking system designed for practical use. UltraMotion performs point cloud queries based on hidden Markov models (HMMs), a novel ultrasonic acoustic ranging method, and an extended Kalman filter (EKF) to predict the locations of all three arm joints, making it the first system offering shoulder locations. Experimental results with only a smartphone and a smartwatch demonstrate the effectiveness of UltraMotion in tracking shoulder, elbow, and wrist locations with impressively small median errors of 6.4 cm, 7.1 cm, and 8.5 cm in real-world environments, outperforming all previous systems, making UltraMotion an ideal choice for daily exercise.

Deep Neural Networks (DNNs) possess powerful prediction capability thanks to their over-parameterization design, although the large model complexity makes it suffer from noisy supervision. Recent approaches seek to eliminate impacts from noisy labels by excluding data points with large loss values and showing promising performance. However, these approaches usually associate with significant computation overhead and lack of theoretical analysis. In this paper, we adopt a perspective to connect label noise with epistemic uncertainty. We design a simple, efficient, and theoretically provable robust algorithm named USDNL for DNNs with uncertainty-based Dropout. Specifically, we estimate the epistemic uncertainty of the network prediction after early training through single Dropout. The epistemic uncertainty is then combined with cross-entropy loss to select the clean samples during training. Finally, we theoretically show the equivalence of replacing selection loss with single cross-entropy loss. Compared to existing small-loss selection methods, USDNL features its simplicity for practical scenarios by only applying Dropout to a standard network, while still achieving high model accuracy. Extensive empirical results on both synthetic and real-world datasets show that USDNL outperforms other methods. Our code is available at https://github.com/kovelxyz/USDNL.

Federated learning (FL) sheds light on efficiently and privately learning from massive Internet of Things (IoT) devices. However, the iterative training and aggregation pose additional stress on the limited energy and availability budgets of clients. In this paper, we discuss two types of availability budgets of IoT clients, including the timing to start participating in FL and the communication budgets due to their constrained energy. We theoretically analyze the effect of availability budgets on FL, based on the availability constraints, by leveraging a decaying quadratic function to prioritize learning from statistically heterogeneous clients during the initial training rounds. We also consider the effects of client availability in terms of their participation to find a balance among clients with varying availability. We present FedCAB, an algorithm applying our theoretical model for the probabilistic rankings of the available clients to select in each round of FL model aggregation. Numerical results show the effectiveness of FedCAB under label distribution skew with a limited communication budget and clients that join the learning process in later rounds. We release the source code of FedCAB at https://github.com/denoslab/FedCAB.

With continuous and accelerated urbanization, a large number of location-based services (LBSs) have shifted from outdoor to indoor. The pervasive smartphone-based localization has been the subject of extensive work, including signals, algorithms, technologies, solutions, and applications. However, no single ubiquitous technology or solution exists for performing indoor positioning similar to the global navigation satellite system (GNSS) in the outdoor environment. The aim of this work is to develop a practical, precise, and economic smartphone-based localization solution. In order to address the challenges of utilizing the limited audible-band acoustic signal in pervasive smartphone localization, i.e., signal detection, correction, and evaluation, we present a low-cost anchor hardware, two-step signal detection method, data-driven pedestrian dead reckoning (PDR), and robust positioning algorithm. Moreover, we further propose acoustic measurement compensation approaches and measurement quality evaluation and control strategy (MQECS) to improve the performance of position estimation. Six phones, including Huawei Mate9, P9 Plus, OnePlus 6, Honor 8, Mi 10, and Google Pixel 3 are used to evaluate the localization performance in three typical wide-area indoor scenarios (i.e., convention center, parking lot, and dining-hall). The total testbed area is accumulated to more than 8800 square meters. The experimental results demonstrate that the proposed method achieves average positioning accuracies of 0.34 m (static) and 0.67 m (dynamic). In addition, the results show that the overall performance, repeatability, and stability are superior for different scenarios and devices.

In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger’s sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.

Recently, the neural network has become a popular technology for pedestrian inertial navigation to avoid the errors caused by the integral part of traditional inertial navigation system and also performs better than Pedestrian Dead Reckoning(PDR). However, researchers who leverage the neural network all discard the magnetic information due to the instability of magnetic field. On account of low-cost inertial measurement unit(IMU), relying on the gyroscope and the accelerometer only will inevitably produce horizontal angular deviation. This small angular deviation will be magnified as the trajectory length increases, and finally, cause positioning drift. Through many experiments and analyses, we discovered that there is a correlation between the magnetic information and the pedestrian’s body direction under a stable magnetic field. Based on the discovery, MAGINS, a neural network inertial navigation system corrected by magnetic information was designed. A data set of motion information including IMU data and real positions was created and utilized to train a network model as the basis of our system. For the sake of the stable and available magnetic information, we designed an algorithm to quantitatively detect the stability of the magnetic field. The heading of navigation can be corrected according to the stability and the correlation mentioned above. The experiment result shows that MAGINS can detect the stability of the magnetic field precisely, and correct the heading properly since the magnetic information will not produce accumulated errors. The positioning effect of MAGINS is better than other pedestrian inertial navigation systems only based on neural network.

The variety of sensors embedded in smartphones makes it possible to develop indoor navigation and localization systems on mobile terminals. However, these cheap sensors are plagued by bias and noise, leading to unbounded system drifts. Inspired by Expectation-Maximization algorithm, this paper proposes to combine zero-velocity detection with gated recurrent unit (GRU) neural networks, make full use of pedestrian motion characteristics, and naturally and accurately split the raw measurements into multiple weakly correlated windows step by step. The GRU is used to exploit dynamic context and predict the polar vector of each window. Several experiments were conducted to test the performance of proposed model, and IONet, a deep learning based inertial odometry model using fixed-size sliding window, was taken as a reference. The results show that the proposed model is able to generate smooth trajectories with high precision. Compared with IONet, the performance of proposed model in turning is better.

Currently, indoor localization technology and indoor location-based services are becoming increasingly important in the area of mobile and ubiquitous computing. However, the design of an indoor location-based system confronts two challenges: 1) achieving high-precision location recognition and 2) identifying what indoor objects actually are (which is called semantic labeling). In this article, we propose AtLAS, an activity-based indoor localization and semantic labeling mechanism. The key idea is that some objects in an indoor environment, such as doors and toilets, determine predictable human behaviors in small areas, which can be reflected in unique sensor readings. AtLAS leverages this idea to determine a user's accurate location by identifying users' activities. Furthermore, we leverage the topological structure of indoor objects to mine the semantic knowledge and label the objects through gained knowledge automatically. To the best of our knowledge, AtLAS is the first attempt to build a system that leverages users' activities to conduct a high-precision indoor localization and semantic labeling system for the case of residences. The experimental results show that AtLAS can achieve a median localization accuracy of 0.57 m, and the system can localize the landmarks with a median accuracy of 0.43 m on average without 5% worst errors. AtLAS can label the objects semantically with a 5.7% false-positive rate and a 5.8% false-negative rate on average.

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

Occlusion is a special challenge in visual tracking, which may cause target template corrupted by background information. In this paper, we propose an adaptive occlusion detection framework for robust tracking against occlusion. The framework consists of a patch tracker, an occlusion detector, a template updater and a search window predictor. The patch tracker applies KCF-based method to track background patch individually, which may occlude target. The occlusion detector searches for background patches occluding target with an adaptive threshold. The template updater evaluates the occlusion state and applies appropriate target template update strategy. The search window predictor adaptively rescales the size of search window based on occlusion state. Experiments in OTB50 demonstrate that our tracker achieves comparable performance compared with other state-of-art trackers and outperforms them in cases of occlusion.

Correlation filter based tracking algorithms have been commonly used in object tracking community. Recently, hand-craft features are replaced by deep convolutional features pre-trained on lager scale image datasets. The low level features with high resolution can locate the position of targets more accurate while the high level features contain more semantic information. In this paper, we construct several single conv-feature correlation filters as weak classifiers. Then, we apply boosting learning method to train a multi conv-features tracker for combining both high resolution features and semantic features. The boosting learner assigns adaptive weights for weak classifiers and the position of target is estimated by the adaptive weighted response map. Experimental results on comprehensive dataset OTB2013 demonstrate that our tracking algorithm can achieve accurate and robust performance compared with baselines and other state-of-art trackers.

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Recently, fully convolutional network (FCN) and dilated convolution have shown significantly improvment in semantic segmentation task. Deep residual network (ResNet) has shown strong ability in object recognition. However, FCN-based methods utilize intermediate layers and spatial context information ineffectively. Repeated 2-step striding in ResNet is harmful for segmentation tasks. In this paper, we propose a new semantic segmentation method based on FCN and ResNet. Here, we combine the dilated convolution designed for semantic segmentation with residual unit to enlarge receptive field of ResNet. Meanwhile, “pre-activation” method is used in dilated residual unit. We propose a new segmentation architecture which intergrate multiple intermediate layers and global context information. We extract low-level features of intermediate layers with multi-path refinement which consists of relu-conv unit and chained residual pooling. Global context is gathered by a pyramid pooling method which is connected to the final output of ResNet. Outputs of these 2 modules are then fused to reach high-resolution prediction. In this way, global and local information from pyramid pooling can be enhanced by multi-path refinement. Fully-connected conditional random field is added as a “post-processing” after fusion to receive accurate boundary performance. Our proposed approach achieves 74.7% IoU on Cityscapes benchmark.

Occlusion is one of the most challenging factors in visual tracking. In this paper, we propose a novel context-based occlusion detection algorithm for robust visual tracking. The basic idea of our algorithm is that occlusion indicates that some background points in previous frame move into the target region in current frame. Our algorithm investigates background patches with background trackers. The occlusion is examined by the a occlusion detector. The template updating strategy is that if occlusion is detected, the target template stops updating. Comprehensive experiments in CVPR2013 Online Objecting Tracking Benchmark (OOTB) show that our tracker achieves comparable performance with other state-of-art trackers.

This paper investigates how to estimate the crowdedness level for crowdsensing buses. Passengers' moving trajectories at the bus stops are able to reflect the crowdedness situation of the bus. And the high accuracy of the existing posture recognition methods for crowdsensing applications can ensure the reliability of the passenger motion monitoring. Based on these observations, we propose a hierarchical-learning-based crowdedness estimation mechanism, namely HCE, to obtain the crowdedness level of a crowdsensing bus. The motion sequence and gait information of a participatory passenger is obtained via the sensing data from sensors in smartphones and can be expressed by eigenvectors. Then the feature vectors are classified as different individual crowdedness levels based on hierarchical support vector machine (SVM) classifier and hidden markov model (HMM). Finally the crowdedness levels of the buses can be reckoned by the estimated crowdedness levels from all the individual passengers. The experimental results show that our mechanism can achieve at least 83% accuracy.

Bone scintigraphy is widely used to diagnose bone diseases. Accurate hotspot segmentation is a critical task for tumor metastasis diagnosis. In this paper, we propose an interactive approach to detect and extract hotspots in thoracic region based on a new multiple instance learning (MIL) method called EM-MILBoost. We convert the segmentation problem to a multiple instance learning task by constructing positive and negative bags according to the input bounding box. In order to be robust against noisy input, we train a region-level hotspot classifier with EM-MILBoost and develop several segmentation strategies based on it. The experimental results demonstrate that our method outperforms other methods and is robust against various noisy input.

Wireless sensor networks (WSNs) are vulnerable to the unfavorable funneling effect. The optimization of WSN clustering is a natural way to suppress the funneling effect. WSN clusters involve the edge effect that was undervalued in existing techniques. We propose an optimal clustering routing protocol GreenOCR to reduce the detrimental influence of the funnel effect and minimize the energy consumption in WSNs. Our work focuses on the approximate unequal optimal clustering and dropping energy consumption arising from the edge effect. First, according to the data repeat rate among overlapped clusters, we estimate the actual data compression ratio to offset the negative influence of the edge effect and save WSN energy. Secondly, we reduce the issue of minimizing the total energy consumption in a WSN to a nonlinear programming (NLP). We have proved that this NLP problem is NP complete. Third, we turn over to exploring an approximate optimal clustering and propose an approximate optimal clustering algorithm. A GreenOCR enabled WSN clustering minimizes the energy consumption in the whole network and extends the lifetime of the WSN. The simulation experiment shows that GreenOCR outperforms its rivals in alleviating the funnel effect.

Summary The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. Copyright © 2015 John Wiley & Sons, Ltd.

The urban road traffic flow condition prediction is a fundamental issue in the intelligent transportation management system. While extracting the high-dimensional, nonlinear and random features of the transportation network is a challenge, which is very useful to improve the accuracy of traffic prediction. In this paper, we propose DeepSense, a novel deep temporal-spatial traffic flow feature learning mechanism, with large scale Taxi GPS traces for traffic prediction. Deep-Sense includes two switchable feature learning approaches. DeepSense exploits a temporal-spatial deep learning approach for traffic flow prediction with the sufficient spatial and temporal taxi GPS traces in dynamic pattern. Meanwhile, Deep-Sense takes advantage of a supplementary temporal sequence segment matching approach with the temporal transformation of traffic flow state for a given road segment when there are not enough traffic traces. Experimental results show that DeepSense can achieve higher prediction accuracy with nearly 5% improvements compared with existing methods.

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper firstly proposes E-cent, a unit bearer currency. It is exchangeable, and participants can utilize it to participate in tasks anonymously. By employing E-cent, we further propose an E-cent-based privacy-preserving incentive mechanism, called EPPI, which exploits a pledge-based participating protocol to encourage participants to participate without revealing privacy and prohibit participants from sending false data. EPPI also takes advantage of a dynamic reward allocation scheme to maximize the value of the services under a budget constraint. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy-preserving in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

For wireless sensor networks deployed to monitor and report real events, event source-location privacy (SLP) is a critical security property. Previous work has proposed schemes based on fake packet injection such as FitProbRate and TFS, to realize event source anonymity for sensor networks under a challenging attack model where a global attacker is able to monitor the traffic in the entire network. Although these schemes can well protect the SLP, there exists imbalance in traffic or delay. In this paper, we propose an Optimal-cluster-based Source Anonymity Protocol (OSAP), which can achieve a tradeoff between network traffic and real event report latency through adjusting the transmission rate and the radius of unequal clusters, to reduce the network traffic. The simulation results demonstrate that OSAP can significantly reduce the network traffic and the delay meets the system requirement.

Recently, System-on-Chip (SoC) technology has been adopted to design smaller, lower-power and cheaper tamper-resistant sensor nodes. In these nodes, we find that there exists a lifetime-secure memory fraction which stores the anterior part of the application executable binary code, namely "fingerprint". We propose a key management protocol based on this secure finger- print-FKM. In this protocol, any pair of nodes can build a secret key by combining two raw key elements randomly selected by both nodes from their fingerprints respectively. To further strengthen the security, we also present two multi-dimension grid key reinforcement schemes. To the best of our knowledge, this paper is the first attempt at the use of application executable binary code itself to develop a key management protocol A thorough analysis shows that FKM supports higher security and superior operational properties while consuming less memory resource compared to the existing key establishment schemes.

This paper describes a wireless sensor network for mine safety monitoring. Based on the characteristics of underground mine gallery and the requirements for mine safety monitoring, we proposed a distributed heterogeneous hierarchical mine safety monitoring prototype system, namely HHMSM This system is capable of monitoring methane concentration, and locating miner. We proposed a novel overhearing-based adaptive data collecting scheme which exploits the redundancy and correlation of the sampling readings in both time and space to reduce traffic and control overhead with a well-bounded offset error for large-scale sensor networks. This mechanism is easy to implement and low-cost compared to other more theoretically based mechanisms such as Kalman filter. Experimental results show that HHMSM achieves better performance on flexibility, correctness, coverage, and lifetime compared with other existing wireless mine safety monitoring systems.

Routing is one of the fundamental but challenging issues in mobile ad hoc networks. During the past several years, a large number of routing protocols have been proposed, which can basically be categorized into three different groups including proactive/table-driven, reactive/on-demand, and hybrid. In this paper, we propose a novel hybrid routing protocol for large scale mobile ad hoc networks, namely HCR (Hybrid Cluster Routing). Here nodes are organized into a hierarchical structure of multi-hop clusters using a stable distributed clustering algorithm. Each cluster is composed of a clusterhead, several gateway nodes, and other ordinary nodes. The clusterhead is responsible for maintaining local membership and global topology information. In HCR, the acquisition of intra-cluster routing information operates in an on-demand fashion and the maintenance of inter-cluster routing information acts in a proactive way. Simulation results show that HCR conduces better scalability, robustness and adaptability to large scale mobile ad hoc networks compared with some well-known routing protocols, e.g. AODV, DSR, and CBRP.

A locality-based clustering peer-to-peer overlay networks (LCO) architecture is introduced in this paper. LCO differs from the pure unstructured P2P networks such as Gnutella in two key aspects. First, LCO partitions peers into clusters such that peers belonging to the same cluster are relatively close to one another in terms of network latency. Multiple floods are initiated for one query, with the character that each flood is restricted within one cluster, hence reducing the unnecessary traffic produced by the topology mismatching between the P2P logical overlay network and the physical underlying network. Second, an efficient inter-cluster index scheme is used in LCO such that the search scope can be retained even though only a few clusters are directly probed. Our simulation results indicate that LCO is efficient in both resource usage and data retrieval.