Graph clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not fully utilize these edge weights. Leveraging edge weights in graph clustering tasks faces two critical challenges. (1) The introduction of edge weights may significantly increase storage space and training time, making it essential to reduce the graph scale while preserving nodes that are beneficial for the clustering task. (2) Edge weight information may inherently contain noise that negatively impacts clustering results. However, few studies can jointly optimize clustering and edge weights, which is crucial for mitigating the negative impact of noisy edges on clustering task. To address these challenges, we propose a contractile edge-weight-aware graph clustering network. Specifically, a cluster-oriented graph contraction module is designed to reduce the graph scale while preserving important nodes. An edge-weight-aware attention network is designed to identify and weaken noisy connections. In this way, we can more easily identify and mitigate the impact of noisy edges during the clustering process, thus enhancing clustering effectiveness. We conducted extensive experiments on three real-world weighted graph datasets. In particular, our model outperforms the best baseline, demonstrating its superior performance. Furthermore, experiments also show that the proposed graph contraction module can significantly reduce training time and storage space.

Continuous category discovery (CCD) aims to automatically discover novel categories in continuously arriving unlabeled data. This is a challenging problem considering that there is no number of categories and labels in the newly arrived data, while also needing to mitigate catastrophic forgetting. Most CCD methods cannot handle the contradiction between novel class discovery and classification well. They are also prone to accumulate errors in the process of gradually discovering novel classes. Moreover, most of them use knowledge distillation and data replay to prevent forgetting, occupying more storage space. To address these limitations, we propose Independence-based Diversity and Orthogonality-based Discrimination (IDOD). IDOD mainly includes independent enrichment of diversity module, joint discovery of novelty module, and continuous increment by orthogonality module. In independent enrichment, the backbone is trained separately using contrastive loss to avoid it focusing only on features for classification. Joint discovery transforms multi-stage novel class discovery into single-stage, reducing error accumulation impact. Continuous increment by orthogonality module generates mutually orthogonal prototypes for classification and prevents forgetting with lower space overhead via representative representation replay. Experimental results show that on challenging fine-grained datasets, our method outperforms the state-of-the-art methods.

Hierarchical Multi-Label Classification (HMC) faces critical challenges in maintaining structural consistency and balancing loss weighting in Multi-Task Learning (MTL). In order to address these issues, we propose a classifier called HCAL based on MTL integrated with prototype contrastive learning and adaptive task-weighting mechanisms. The most significant advantage of our classifier is semantic consistency including both prototype with explicitly modeling label and feature aggregation from child classes to parent classes. The other important advantage is an adaptive loss-weighting mechanism that dynamically allocates optimization resources by monitoring task-specific convergence rates. It effectively resolves the ''one-strong-many-weak'' optimization bias inherent in traditional MTL approaches. To further enhance robustness, a prototype perturbation mechanism is formulated by injecting controlled noise into prototype to expand decision boundaries. Additionally, we formalize a quantitative metric called Hierarchical Violation Rate (HVR) as to evaluate hierarchical consistency and generalization. Extensive experiments across three datasets demonstrate both the higher classification accuracy and reduced hierarchical violation rate of the proposed classifier over baseline models.

In recent times, gait recognition, a type of biometric identification, has been widely used for area access control and smart homes. It improves convenience, privacy, and personalized experiences. Contemporary academic inquiry centers on privacy-preserving wireless sensing solutions as substitutes for computer vision. Yet, prevailing strategies heavily lean on abstract features, leading to inherent limitations in interpretability and stability. Fortunately, the widespread utilization of smart speakers has opened up opportunities for acoustic sensing, making it possible to extract more interpretable features. In this paper, we further push the limit of acoustic recognition with visual interpretability by sequentially visualizing fine-grained acoustic human gait features. The construction of initial gait profiles involves matrixing and compressing multipath gait echoes, resulting in imperceptible gait indications. Interpretability is then achieved through novel micro-range profiles, incorporating innovations such as clutter elimination using the Mobile Target Detector (MTD), compensation for farther echo strength, and subtraction of macro torso migration. These interpretable gait profiles offer practical benefits by enhancing data utilization, optimizing abnormal data handling, and improving model stability. Extensive evaluations with an open experimental scenario have been conducted to demonstrate accuracy reaching 97.5% in general, and robust performance against impacts from various practical factors.

With the rapid development of intelligent driving and the Internet of Vehicles, network security in vehicles has received wide attention. In this paper, we study the problem of abnormal ECU sourcing in the Controller Area Network (CAN) which is a typical and widely used in-vehicle communication protocol. We find that the simple clock-based fingerprints adopted by many related methods cannot work effectively when environmental interference exists. This is because interference makes the measured clock offset deviate significantly from the real value. To overcome the above limitation, we propose an Interference-Tolerant abnormal ECU Sourcing (ITAS) approach based on a multi-dimensional clock fingerprint. ITAS decomposes the measured clock offset, constructs a multi-dimensional clock fingerprint, and maps fingerprints to respective ECUs to help abnormal ECU sourcing. The performance evaluation based on real vehicle CAN bus data collected from a Honda Civic demonstrates the strong anti-interference capability of the proposed ITAS with an over 96% accuracy for abnormal ECU sourcing.

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF-based mechanisms. However, existing approaches for portable devices solely track a single target, incapable of the ubiquitous and highly challenging multi-target situations such as double-hand multimedia controlling and multi-player gaming. In this paper, we proposeAMT$^+$, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. The challenge of multi-target occlusion is effectively addressed by employing multiple speaker-microphone pairs. However, the unique challenge raised by MIMO is the superposition of multi-source signals due to the cross-correlation among speakers. Initially, we tackle this challenge by designing a weak cross-correlation signal to reduce interference passively. InAMT$^+$, we’ve further integrated self-interference cancellation for active minimize interference. The most distinguishing advantage ofAMT$^+$lies in the elimination of the raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles.AMT$^+$employs Doppler filtering over delay subtraction for echo suppression. Further, by non-particle target reflections modeling results, we introduce a distance-projection-based method for continuous target identification and tracking. Implemented on commercial smartphones,AMT$^+$achieves on average 0.54 cm, 1.37 cm, and 2.13 cm errors for single, double, and triple target tracking respectively, and on average 97.0% classification accuracy for 14 controlling gestures.

Recently, as fitness has become a popular part of people’s lives, the intention to record fitness processes and assess the standards of fitness movements has grown increasingly keen. However, the existing approaches have some limitations, for example, wearable devices can hinder users’ fitness activities; computer vision–based solutions pose the risk of privacy breach, and so on. Fortunately, we observed that smartspeaker, acoustic-based sensing is a promising method of activity monitoring. In this article, we propose Afitness, an acoustic-based sensing system that enables non-intrusive, passive, and high-precision fitness detection. Afitness has the following three innovations. (i) We utilize pulse compression to generate high-precision motion distance images on commercial devices that can be visually recognized. (ii) We propose a data augmentation algorithm, which also incorporates transfer learning to greatly reduce the pressure of data collection. (iii) We exploit incremental learning techniques that allow Afitness to improve the portability of our system and recognize new actions. Overall, Afitness achieves acoustic signal interpretability and environmental reliability detection.

Acoustic target tracking has shown significant potential for contactless human-computer interaction. However, most existing acoustic 2-D tracking approaches for portable devices require at least one speaker and two microphones, incapable for universal devices. In this paper, we propose NearTracker, a contactless acoustic tracking system, achieves 2-D target tracking with only one speaker and one microphone (i.e., Single Input Single Output, SISO). With the help of a nearby reflector, the additional valuable echoes from target are combined for positioning. Actually, the dynamic interferences from non-target echoes pose huge challenges for target echo extraction. NearTracker extracts and enhances these faint target echoes with novel signal processing methods and estimates the target’s location accurately via a designed particle filter algorithm. Extensive experiments show that our system achieves on average 1.36 cm error for 2-D target tracking, which can satisfy most devices and application scenarios.

A practical challenge common to state-of-the-art acoustic gesture recognition techniques is to adaptively respond to intended gestures rather than unintended motions during the real-time tracking on human motion flow. Besides, other disadvantages of under-expanded sensing space and vulnerability against mobile interference jointly impair the pervasiveness of acoustic sensing. Instead of struggling along the bottlenecked routine, we innovatively open up an independent sensing dimension of acoustic 2-D hand-shape imaging. We first deductively demonstrate the feasibility of acoustic imaging through multiple viewpoints dynamically generated by hand movement. Amaging, hand-shape imaging triggered gesture recognition, is then proposed to offer adaptive gesture responses. Digital Dechirp is novelly performed to largely reduce computational cost in demodulation and pulse compression. Mobile interference is filtered by Moving Target Indication. Multi-frame macro-scale imaging with Joint Time-Frequency Analysis is performed to eliminate image blur while maintaining adequate resolution. Amaging features revolutionary multiplicative expansion on sensing capability and dual dimensional parallelism for both hand-shape and gesture-trajectory recognition. Extensive experiments and simulations demonstrate Amaging’s distinguishing hand-shape imaging performance, independent from diverse hand movement and immune against mobile interference. 96% hand-shape recognition rate is achieved with ResNet18 and 60× augmentation rate.

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF based mechanisms. However, existing approaches for portable devices solely track single target, incapable for the ubiquitous and highly challenging multi-target situation such as double-hand multimedia controlling and multi-player gaming. In this paper, we propose AMT, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. Targets' absolute distance are simultaneously ranged by performing multi-lateration locating with multiple speaker-microphone pairs. The unique challenge raised by MIMO is the superposition of multisource signals due to the cross-correlation among speakers. We tackle this challenge by applying Zadoff-Chu(ZC) sequences with strong auto-correlation and weak cross-correlation. The most distinguishing advantage of AMT lies in the elimination of target raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles. Concerning the multipath echoes reflected by each non-particle target, we define the novel concept of primary echo to best represent target movement. AMT then improves tracking accuracy by detecting primary echo and filtering out minor echoes. Implemented on commercial smartphones, AMT achieves on average 1.13 cm and 2.46 cm error for single and double target tracking respectively and on average 97% accuracy for 6 controlling gestures recognition.

Coverage-based greybox fuzzing (CGF) has been widely studied and commonly used for software vulnerability detection. Existing CGF fuzzers fairly allocate execution time for each mutation operation to generate test cases. However, the fair-time-allocation strategy is revealed to be inefficient by our significant experimental observation that different operations have heterogeneous effectiveness on coverage. Those ineffective operations with vast test cases thus occupy the majority of limited runtime, reducing the opportunities for effective operations to explore more paths and find potential vulnerabilities.In this paper, we propose a novel operation-oriented time allocation strategy OTA, which dynamically allocates operation execution time in real time to cope with the effectiveness variation per operation. OTA has three distinguishing advantages: (1) the execution time per operation is novelly initialized on demand and program-dependent; (2) the execution time for each operation is dynamically weighted by its real-time effectiveness on exploring new coverage; (3) the determination of the execution time per operation is well controlled to achieve a quick convergence. Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection abilities of OTA, which substantially outperforms 5 state-of-the-art fuzzers. In addition, OTA exposes 18 previously unknown vulnerabilities in 6 well-tested programs with 13 confirmed with new CVE IDs.

Nowadays most metro advertising systems schedule advertising slots on digital advertising screens to achieve the maximum exposure to passengers by exploring passenger demand models. However, our empirical results show that these passenger demand models experience uncertainty at fine temporal granularity (e.g., per min). As a result, for fine-grained advertisements (shorter than one minute), a scheduling based on these demand models cannot achieve the maximum advertisement exposure. To address this issue, we propose an online advertising approach, called FineUDM, based on the uncertain passenger demand modeling for both entering passengers and exiting passengers. FineUDM combines coarse-grained statistical demand modeling and fine-grained real-time demand modeling by leveraging historical passenger demands, real-time card-swiping records, and passenger mobility patterns. Based on this uncertain demand model, it schedules advertising time online based on robust receding horizon control to maximize the advertisement exposure. We evaluate the proposed approach based on an one-month sample from our 530 GB real-world metro fare dataset with 16 million cards. The results show that our approach provides a 61.5 percent lower traffic prediction error and 20 percent improvement on advertising efficiency on average.

An efficient and low‐cost communication system has great significance in maritime communication, but it faces enormous challenges because of high communication costs, incomplete communication infrastructure, and inefficient routing algorithms. Delay Tolerant Vessel Networks (DTVNs), which can create low‐cost communication opportunities among vessels, have recently attracted considerable attention in the academic community. Most existing maritime ad hoc routing algorithms focus on predicting vessels’ future contacts by mining coarse‐grained social relations or spatial distribution, which has led to poor performance. In this paper, we analyze 3‐year trajectory data of 5123 fishery vessels in the China East Sea. Using entropy theory, we observe that the trajectory of the vessel has strongly spatial‐temporal distribution regularity, especially when previous states were given. To predict accurate future trajectories, we develop a long‐term accurate trajectory prediction model by improving the Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Based on predicted trajectories and the confident degree of each prediction step, we propose a series of routing algorithms called TPR‐DTVN to achieve efficient communication performance. Finally, we carry out simulation experiments with extensive real data. Compared with existing algorithms, the simulation results show that TPR‐DTVN can achieve a higher delivery ratio with lower cost and transmission delay.

Internet of Things realizes the leap from traditional industry to intelligent industry. However, it makes edge devices more vulnerable to attackers during processing perceptual data in real time. To solve the above problem, we use the zero‐sum game to build the interactions between attackers and edge devices and propose an antiattack scheme based on deep reinforcement learning. Firstly, we make the k NN‐DTW algorithm to find a sample that is similar to the current sample and use the weighted moving mean method to calculate the mean and the variance of the samples. Secondly, to solve the overestimation problem, we develop an optimal strategy algorithm to find the optimal strategy of the edge devices. Experimental results prove that the new scheme improves the payoff of attacked edge devices and decreases the payoff of attackers, thus forcing the attackers to give up the attack.

Freehand exercises help improve physical fitness without any requirements on devices, or places (e.g., gyms). Existing fitness assistant systems require wearing smart devices or exercising at specific positions, which compromises the ubiquitous availability of freehand exercises. This work proposes MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver. MobiFit monitors the ubiquitous cellular signals sent by the base station and provides accurate repetition counting, exercise type recognition, and workout quality assessment without any attachments to the human body. To design MobiFit, we first analyze the characteristics of the received cellular signal sequence during freehand exercises through experimental studies. Based on the observation, we construct the analytic model of the received signals. Guided by the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis, and extracts low-frequency features from each repetition for type recognition. We have implemented the prototype of MobiFit and collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1%, and low repetition duration estimation error within 0.3s. Besides, the experiments show that MobiFit works both indoor and outdoor, and supports multiple users exercising together.

External wireless interfaces and the lack of security design of controller area network (CAN) standards make it vulnerable to CAN-targeting attacks. Unfortunately, various defense solutions have been proposed merely to detect CAN intrusion attacks, while only a few works are devoted to intrusion source identification. Demonstrated by our experimental studies, the most advanced IDS with intrusion source identification, which is based on the physical feature fingerprints of the in-vehicle Electronic Control Units (ECUs), will fail when the temperature changes. In this paper, we innovatively propose temperature-varied fingerprinting, called TVF, for CAN intrusion detection and intrusion source identification. Motivated by the remarkable observation that the physical feature of an ECU, i.e., its clock offset, changes linearly with the temperature of ECUs, the concept of temperature-varied fingerprints is proposed. Then, for a severe intrusion case, we provide an advanced TVF for further supplemented and expanded. The proposed advanced temperature-varied fingerprinting is implemented, and extensive performance evaluation experiments are conducted in both CAN bus prototype and real vehicles. The experimental results illustrate the effectiveness and performance of advanced TVF.

Communication in the ocean vessel wireless ad hoc networks which currently rely on shore-based cellular stations, is expensive and can only be used near the coast. It is a big challenge for ocean vessels to achieve economic and large-scope communications, due to the rare encounter opportunities and highly dynamic vessel mobilities. Previous works on routing algorithms for vessel wireless ad hoc networks did not take into account the social characteristics of ocean vessel networks. According to our empirical study on real trajectories of more than 7,000 ocean vessel recorded by the Vessel Monitoring System (VMS), we have made innovative observations. Firstly, there exist stable social connections among those vessels frequently encounter with each other. Moreover, stable social communities can be discovered for those ocean vessels with long-term connections. Inspired by the significant observations, two unicast routing approaches based on social society characteristics are proposed for wireless ocean vessel ad hoc networks, i.e., familiarity based routing (FBR) and community based routing (CBR). The key metric for message forwarding decision making of FBR is the expected multi-hop encounter probability of the potential forwarder with the message receiver. The expected multi-hop encounter probability is derived from the raised definition of familiarity, which measures the connection strengths between vessels. The proposed CBR forwards messages based on the metric of inter-community betweenness centrality of a potential forwarder, which describes the bridge capability between two communities. Extensive trace-driven simulations based on real ocean vessel trajectories are performed and the evaluation results demonstrate the significant performance achieved by the proposed approaches. The transmission cost has been largely reduced with comparable delivery ratio and delay.

The in-vehicle controller area network(CAN) provides reliable communications among ECUs, whereas the lack of security design of CAN protocols makes it vulnerable to CAN targeting attacks. Unfortunately, existing CAN intrusion detection systems merely recognize fabricated CAN messages while only little work are devoted to intrusion source identification. Demonstrated by our experimental study, the state-of-the-art source ECU identification approaches, which are based on physical ECU fingerprints, will fail when ECU temperature varies. In this paper, we innovatively propose temperature-varied fingerprinting, called TVF, for CAN intrusion detection and source ECU identification. Inspired by the significant observation that the clock offset of a specific ECU, i.e., its fingerprint, varies with the environment temperature of the ECU, the concept of temperature-varied ECU fingerprints are proposed and exploited to improve source identification accuracy in real-world vehicle CAN intrusion cases. The proposed temperature-varied fingerprinting is implemented and extensive performance evaluation experiments are conducted in both CAN bus prototype and real vehicles. The experimental results demonstrate the efficacy of the proposed TVF.

We consider the crucial problem of maximizing the social welfare of a vehicular participatory sensing system, where the system's social welfare is measured by the amount of sensing data delivered to a central platform through a vehicular ad hoc network. The key to the problem is to control network stability since both network congestion and idleness will slump system social welfare. However, several great challenges exist. First, limited vehicle-to-vehicle (V2V) link capacity and vehicle buffer size will lead to heavy network congestion when each individual vehicle blindly injects too much data into the network hoping to get more rewards. Second, the highly dynamic network topology and stochastic inter-vehicle contacts have a serious impact on the performance of multi-hop data transmission. Third, vehicles need to be practically rewarded based on their sensing and transmission cost, which, however, greatly vary among vehicles. To tackle the aforementioned challenges, we propose a distributed backpressure control approach, the first work to the best of our knowledge, to maximize the social welfare while balancing network stability for a vehicular participatory sensing system. Combining vehicular network properties and Lyapunov optimization techniques, individualized strategies are developed for each participant to control its sensing rate, make its own routing decisions, and set its own price for data relaying. Formally proved by rigorous theoretical analysis, the social welfare achieved by the proposed approach is comparative to the optimum performance. In addition, extensive data-driven simulations based on real taxi GPS traces have been conducted, and the results confirm the efficacy of the proposed algorithm.

In this article, we propose a transit service Feeder to tackle the last-mile problem, that is, passengers’ destinations lay beyond a walking distance from a public transit station. Feeder utilizes ridesharing-based vehicles (e.g., minibus) to deliver passengers from existing transit stations to selected stops closer to their destinations. We infer real-time passenger demand (e.g., exiting stations and times) for Feeder design by utilizing extreme-scale urban infrastructures, which consist of 10 million cellphones, 27 thousand vehicles, and 17 thousand smartcard readers for 16 million smartcards in a Chinese city, Shenzhen. Regarding these numerous devices as pervasive sensors, we mine both online and offline data for a two-end Feeder service: a back-end Feeder server to calculate service schedules and front-end customized Feeder devices in vehicles for real-time schedule downloading. We implement Feeder using a fleet of vehicles with customized hardware in a subway station of Shenzhen by collecting data for 30 days. The evaluation results show that compared to the ground truth, Feeder reduces last-mile distances by 68% and travel time by 56%, on average.

Delivering data from source vehicles to infrastructures, or convergecast, is a fundamental operation in vehicular networks. However, the network capacity of vehicular network is always limited because of scarce inter-vehicle contacts. Thus, throughput maximization of convergecast in vehicular networks is of great importance. The unique characteristics of vehicular networks, however, present great challenges including frequent connection unavailability and opportunistic contacts. We propose an approach called ConvergeCode for improving the convergecast throughput in vehicular networks, which employs random linear coding for packet delivery. A vehicle randomly combines all received coded data and forwards it to any contacted vehicles. Through extensive empirical study based on the two large datasets of real GPS traces, we make the key observation that significant throughput gain can be achieved by using network coding but a serious fairness issue arises. In this paper, we study the problem of maximizing the throughput of convergecast in vehicular networks at the same time enhancing the fairness among different source nodes. We first formulate the problem of allocating inter-vehicle contacts as a lexicographical max-min multi-source flow problem, and then develop an efficient approximation algorithm with ε-approximation guarantee. Simulations based on real vehicular GPS traces have been performed and results show that the throughput is improved by 74-110 percent while the lexicographical max-min fairness is achieved.

Nowadays most metro advertising systems schedule advertising slots on digital advertising screens to achieve the maximum exposure to passengers by exploring passenger demand models. However, our empirical results show that these passenger demand models experience uncertainty at fine temporal granularity (e.g., per min). As a result, for fine-grained advertisements (shorter than one minute), a scheduling based on these demand models cannot achieve the maximum advertisement exposure. To address this issue, we propose an online advertising approach, called EveryoneCounts, based on an uncertain passenger demand model. It combines coarse-grained statistical demand modeling and fine-grained Bayesian demand modeling by leveraging realtime card-swiping records along with both passenger mobility patterns and travel periods within metro systems. Based on this uncertain demand model, it schedules advertising time online based on robust receding horizon control to maximize the advertisement exposure. We evaluate the proposed approach based on an one-month sample from our 530 GB real-world metro fare dataset with 16 million cards. The results show that our approach provides a 61.5% lower traffic prediction error and 20% improvement on advertising efficiency on average.

In this paper, we propose a transit service Feeder to tackle the last-mile problem, i.e., passengers' destinations lay beyond a walking distance from a public transit station. Feeder utilizes ridesharing-based vehicles (e.g., minibus) to deliver passengers from existing transit stations to selected stops closer to their destinations. We infer real-time passenger demand (e.g., exiting stations and times) for Feeder design by utilizing extreme-scale urban infrastructures, which consist of 10 million cellphones, 27 thousand vehicles, and 17 thousand smartcard readers for 16 million smartcards in a Chinese city Shenzhen. Regarding these numerous devices as pervasive sensors, we mine both online and offline data for a two-end Feeder service: a back-end Feeder server to calculate service schedules; front-end customized Feeder devices in vehicles for real-time schedule downloading. The evaluation results show that compared to the ground truth, Feeder reduces last-mile distances by 68% and travel time by 52% on average.

Multicast is a crucial routine operation for vehicular networks, which underpins important functions such as message dissemination and group coordination. As vehicles may distribute over a vast area, the number of vehicles in a given region can be limited which results in sparse node distribution in part of the vehicular network. This poses several great challenges for efficient multicast, such as network disconnection, scarce communication opportunities and mobility uncertainty. Existing multicast schemes proposed for vehicular networks typically maintain a forwarding structure assuming the vehicles have a high density and move at low speed while these assumptions are often invalid in a practical vehicular network. As more and more vehicles are equipped with GPS enabled navigation systems, the trajectories of vehicles are becoming increasingly available. In this work, we propose an approach called TMC to exploit vehicle trajectories for efficient multicast in vehicular networks. The novelty of TMC includes a message forwarding metric that characterizes the capability of a vehicle to forward a given message to destination nodes, and a method of predicting the chance of inter-vehicle encounter between two vehicles based only on their trajectories without accurate timing information. TMC is designed to be a distributed approach. Vehicles make message forwarding decisions based on vehicle trajectories shared through inter-vehicle exchanges without the need of central information management. We have performed extensive simulations based on real vehicular GPS traces and compared our proposed TMC scheme with other existing approaches. The performance results demonstrate that our approach can achieve a delivery ratio close to that of the flooding-based approach while the cost is reduced by over 80 percent.

Wireless sensor networks (WSNs) are gaining popularity in practical monitoring and surveillance applications. Because of the limited energy of sensor nodes, many WSNs work in a low duty cycle mode to effectively extend their network lifetime. However, low duty cycling also decreases transmission efficiency and makes data gathering more challenging. By exploiting the redundancy of in real sensing data, we propose a novel and distributed approach for data gathering in wireless sensor networks, employing the compressed sensing theory. Instead of selecting a fixed sink, all data can be retrieved from an arbitrary node within the network. Moreover, we use sequential observations to dynamically fit the sparsity of various data sets. With extensive simulations, we show that our approach is efficient with tunable accuracy in different node duty cycles.

This paper considers the crucial problem of event detection and localization with sensor networks, which not only needs to detect occurrences but also to determine the locations of detected events and event source signals. It is highly challenging when taking several unique characteristics of real-world events into consideration, such as simultaneous emergence of multiple events, overlapping events, event heterogeneity and stringent requirement on energy efficiency. Most of existing studies either assume the oversimplified binary detection model or need to collect all sensor readings, incurring high transmission overhead. Inspired by spatially sparse event occurrences within the monitoring area, we propose a compressive sensing based approach called CED, targeting at multiple heterogeneous events that may overlap with each other. With a fully distributed measurement construction process, our approach enables the collection of a sufficient number of measurements for compressive sensing based data recovery. The distinguishing feature of our approach is that it requires no knowledge of, and is adaptive to, the number of occurred events which is changing over time. We have validated the signal attenuation event model through testbed experiments with TelosB motes. Extensive simulation results demonstrate that our approach can achieve high detection rate and localization accuracy while incurring modest transmission overhead.

Geocast in vehicular networks aims to deliver a message to a target geographical region, which is useful for many applications such as geographic advertising. This is a highly challenging task in vehicular network environments due to the rare encounter opportunities and uncertainty caused by vehicular mobility. As more vehicles are equipped with on-board navigation systems, vehicle trajectories are ready for exploitation. We observe that a vehicle has a higher capability of delivering a message to the target region if its own future trajectory or trajectories of those vehicles to be encountered overlap the target region. Motivated by this observation, we develop a message forwarding metric, called coverage capability, to characterize the capability of a vehicle to successfully geocast the message. When calculating the coverage capability, we are facing the major challenge raised by the absence of accurate vehicle arrival time. Through an empirical study using real vehicular GPS traces of 2,600 taxis, we verify that the travel time of a vehicle, which is modeled as a random variable, follows the Gamma distribution. The travel time modeling helps us to make accurate predictions for inter-vehicle encounters. We perform extensive trace-driven simulations and the results show that our approach achieves 37.4 percent higher delivery ratio and 43.1 percent lower transmission overhead comparing with GPSR which is a representative geographic routing protocol.

Vehicular networks have become a promising platform for large-scale urban sensing. On-demand data retrieval is a crucial operation for many applications of vehicular networks. It is particularly challenging, however, to achieve high performance of on-demand data retrieval in sparse vehicular networks. Data replication based on random linear network coding can solve the coupon collection problem but suffers the problem of unnecessary data replications which waste the precious communication opportunities of sparse vehicular networks. With real traces we reveal the existence of community structures in vehicular networks, which causes excessive linearly correlated blocks. Motivated by the important observation, we propose a community-aware data replication scheme based on random linear network coding. To reduce the excessive correlated coded blocks because of community structures, we make probabilistic control on the data replication phase, taking the community structures into account. It is demonstrated through extensive simulations that the community-aware scheme can effectively save up to 50% communication opportunities whiling achieving high performance of on-demand data retrieval.

There are increasing interests on mobile opportunistic networks which have promising applications. Constructing a mobile backbone can effectively improve the packet delivery performance of a mobile opportunistic network by excluding poor relay nodes and reducing packet collisions. However, it is highly challenging to construct an effective mobile backbone because of the absence of the quantitative relationship between the network performance and the selection of backbone nodes, and expositive search space. As nodes exhibit clear sociality observed in previous studies, We explicitly take such node sociality into account when computing the backbone for mobile opportunistic networks and we incrementally propose three algorithms for computing the mobile backbone. Trace-driven simulations have been conducted and simulation results demonstrate that the sociality-aware algorithms can achieve low delivery delay and high delivery ratio.