Smart home hubs have significantly improved everyday home life by serving as central control units that connect and manage various devices, such as lights, door locks, curtains, and cameras. However, the limited CPU and memory resources of these hubs hinder the execution of multiple intelligent tasks, such as human activity recognition, image classification, and speech recognition. To fully utilize these resources, we proposeS-HUB, a scalable deep neural network (DNN) fusion framework for smart home hubs capable of handling multiple tasks. In the offline phase, we apply DNN pruning, weight virtualization, and re-fusion to create a unified model that dynamically scales across tasks. In the online phase, we design a DNN scheduling optimizer to achieve optimal multitask inference while adhering to resource constraints. Finally, comparative experiments and evaluations in smart home scenarios are conducted to assess the performance ofS-HUBacross various tasks, including speech recognition, object detection, gesture recognition, food recognition, and fall detection. Experimental results show that compared to two state-of-the-art baselines,S-HUBachieves an average task processing time of 3 seconds (at least 14% faster) under accuracy constraints, and an average accuracy loss rate of 4.21% (at least 68% lower) under latency constraints. In unconstrained scenarios, it consistently delivers the best overall performance (2.99 seconds and 2.94% loss), demonstrating the scalability and effectiveness of our fusion method in handling performance-resource trade-offs across tasks.

Multi-modal human activity recognition (HAR) is a key technology for a wide range of applications and has received widespread attention in recent years. However, the difficulty of achieving generalizability in multi-modal sensing models, combined with heterogeneous and unlabeled downstream data, significantly hinders their broader adoption. In this work, we proposePFHAR, a unified framework for practically adopting multi-modal foundation HAR model to target user groups.PFHARuses a novel dynamic masked contrastive learning method to pre-train a foundation model on various heterogeneous public HAR datasets, ensuring strong generalizability across different modal combinations. It then adopts semi-supervised edge-cloud collaborative learning to fine-tune the pre-trained model with heterogeneous and unlabeled local data, adapting it for the target user group. Our evaluations on public and self-collected datasets demonstrate thatPFHARsignificantly outperforms SOTA baselines in both the pre-training and edge-cloud collaborative fine-tuning stages.

Live volumetric video streaming provides immersive and interactive experiences. However, state-of-the-art live streaming systems need excessive bandwidth, exceeding the bandwidth capabilities of common mobile networks. A basic solution is to eliminate the redundancy of the original captured data and compress the created volumetric video. However, existing approaches either incorrectly discard normal data or encode the data inefficiently. In this paper, we propose LiVo, a bandwidth-efficient Live Volumetric video streaming system that comprises (i) a compact capture method that efficiently eliminates superfluous points from multiple partial point clouds, and (ii) a compact attribute encoding method that eliminates the spatial-temporal redundancy by proximity-reserved attribute mapping. We implement a prototype of LiVo using commodity devices and extensively evaluate its performance. Our results demonstrate that LiVo achieves bandwidth-efficient live volumetric video streaming and supports transmission under common mobile networks for the first time. Compared with state-of-the-art systems, LiVo reduces bandwidth consumption by at least 47.93%.

Millimeter-wave (mmWave) radar enables privacy-preserving gesture recognition but suffers from limited training data, particularly for lying postures. Existing mmWave radar data generation methods are ineffective due to insufficient 2D video data. To this end, we design a novel system named Venus to generate realistic radar data for lying postures using few 2D videos, which addresses two key challenges including i) the simulation of diverse reflected signals and ii) few real-world data leading to low data fidelity. Venus consists of two key components: (i) a gesture sequence generation and signal simulation network, which combines several key modules, movement information extractor, spatio-temporal latent diffusion model, and mmWave signal simulator, to generate diverse gesture vertex sequences under certain conditions and simulate signal propagation characteristics to obtain coarse radar data; (ii) a meta-learning domain adaption network generates realistic radar data with few real-world data via ''meta-learning'' strategy. Extensive experiments on both generated and self-collected datasets demonstrate that Venus significantly outperforms state-of-the-art methods in recognizing gestures performed in lying postures.

Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances; ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories; iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees.

Massive urban-scale vehicle trajectories benefit various downstream applications. However, trajectories collected from existing sensing systems are often incomplete, necessitating the recovery of coarse-grained trajectories. Considering that mobility knowledge learned from a single system is less representative of all vehicles or covers only partial road segments, it becomes essential to combine diverse data from multiple systems to support trajectory recovery. Therefore, we learn the impacts of mobility intentions and dynamic traffic conditions on the movement of vehicles from trajectories aggregated across different systems to recover their travel routes on unobservable road intersections. Nonetheless, aggregating raw data across multiple systems raises privacy concerns. This data isolation compounds challenges in acquiring comprehensive mobility intentions and traffic conditions, thereby impairing recovery performance. In this paper, we proposeCrossTrace, a two-stage framework for privacy-aware cross-system trajectory recovery: in theTraffic Condition Inferencestage, a Split Learning pipeline with a multi-view graph neural network is utilized to infer complete traffic conditions for all road segments; in theTrajectory Recoverystage, a Federated Learning pipeline with dedicated modules is utilized to recover missing points by fusing inferred traffic conditions and mobility intentions. Extensive experiments on two large-scale trajectory datasets demonstrate thatCrossTraceoutperforms all alternative schemes.

Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition. However, the lack of rich and fine-grained radar datasets hinders progress in developing generalized deep learning models for gesture recognition across various user postures (e.g., standing, sitting), positions, and scenes. To remedy this, we resort to designing a software pipeline that exploits wealthy 2D videos to generate realistic radar data, but it needs to address the challenge of simulating diversified and fine-grained reflection properties of user gestures. To this end, we designG3Rwith three key components: i) agesture reflection point generatorexpands the arm's skeleton points to form human reflection points; ii) asignal simulation modelsimulates the multipath reflection and attenuation of radar signals to output the human intensity map; iii) anencoder-decoder modelcombines asampling moduleand afitting moduleto address the differences in number and distribution of points between generated and real-world radar data for generating realistic radar data. We implement and evaluateG3Rusing 2D videos from public data sources and self-collected real-world radar data, demonstrating its superiority over other state-of-the-art approaches for gesture recognition.

Edge-cloud collaborative learning emerges as a promising paradigm for adapting pre-trained deep neural network (DNN) models to the ever-changing edge data environments and specific downstream tasks. However, the heterogeneity of edge devices and unlabeled local data hinder the effectiveness of existing collaborative learning approaches. To address the above issues, we proposeACL, a novel adaptive edge-cloud collaborative learning paradigm for heterogeneous devices with unlabeled local data. InACL, we first useFedNAS, a neural architecture search algorithm designed for collaborative learning to generate a customized model on each participating device, and then a lightweight semi-supervised collaborative learning frameworkHSSCLis used to fine-tune the pre-trained DNN model. Compared with the SOTA collaborative learning approaches,ACLachieves significant accuracy improvement, averaging 31.5% for image classification and 15.5% for object detection. Furthermore, it reduces time overhead by 3.1-5.1× and memory overhead by 6.3-12.5×. We will release our models and tools.

The increasingly prevalent livecast streaming causes expensive bandwidth costs and delivery capacity challenges for the content delivery network (CDN) service. As an emerging paradigm, edge computing offers new opportunities to address this issue. The existing works are limited to the data volume pricing model. In contrast, we focus on the 95th-percentile pricing model, which is adopted by many large-scale livecast systems. We propose a Cost-aware Edge-assisted Livecast system (CEL) to minimize the bandwidth cost, consisting of two components: 1) the Shapley values are leveraged to model the actual bandwidth costs for the CDN and edge servers in different time slots, together with acceleration technologies for fast Shapley value estimation and 2) a greedy request scheduling algorithm with theoretical guarantees is proposed to solve the online request scheduling problem, which is NP-hard. Based on real-world data from an operational livecast system, our experiments demonstrate thatCELis time-efficient and achieves at least 14.81% bandwidth cost savings compared with four state-of-the-art methods.

Obtaining urban-scale vehicle trajectories is essential to understand urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose$F^{3}VeTrac$, an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ($F^{3}$) trajectories.$F^{3}VeTrac$utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate$F^{3}VeTrac$with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Moreover, we present a downstream application of$F^{3}VeTrac$for traffic condition estimation, which obtains obvious performance gains.

Millimeter wave radar is gaining traction recently for enabling privacy-preserving human sensing. However, the lack of large-scale, dynamic radar datasets impedes progress in developing robust and generalized deep learning models for mobile sensing applications. To address this problem, we resort to designing a software pipeline that leverages wealthy dynamic videos to generate synthetic radar data, but it faces two key challenges including i) incorrect camera and human positions leading to erroneous superposition of signal intensity and ii) the signal reflection of the background and humans in mobile scenes. To this end, we designMidas++to utilize rich videos to generate realistic radar data via two components: (i) ahuman mesh fitting and calibrationcomponent calculates the camera ego-motion parameters to calibrate the extracted human positions; (ii) areflection and noise signal estimationcomponent combines several key modules,depth prediction,reflection model, andspatiotemporal noise estimation, to output coarse radar data, followed by aU-Netmodel to generate realistic radar data. We implement and evaluateMidas++with video data from public data sources and real-world radar data, demonstrating thatMidas++outperforms other state-of-the-art approaches for both activity recognition and object detection tasks.

The diversified mobility on demand (MoD) systems integrate both traditional fuel vehicles and green transportation tools (e.g. shared bicycles and shared e-bikes), which can not only reduce the fleet size of traditional fuel vehicles but also address the demand for short-distance travel and alleviate environmental pollution. However, despite having a variety of travel tools, the existing MoD systems neglect the guidance on passengers according to their preferences and travel characteristics and thus lead to the failure of effective cooperation among multiple travel modes and additional waste of resources. This inspired us to design a novel order allocation mechanism for diversified MoD systems. Specifically, we construct a heterogeneous order graph based on the order sets, transform the minimum fleet problem into the minimum trajectory coverage problem on the heterogeneous order graph and propose a learning-based order allocation method LAMD$^{2}$containing three modules. i) The online breadth-first order search framework fully considers the characteristics of different travel modes and the interaction of multiple vehicles, and then leverages the competitive mechanism to well handle the heterogeneity of travel modes and improve the overall efficiency. ii) The multi-semantic travel mode selection module analyzes users' preferences for diversified travel modes based on multi-semantic historical travel data and then determines the service mode based on the similarity of order spatiotemporal characteristics. iii) The Reinforcement Learning (RL)-based order evaluation module evaluates the long-term benefits of expanding existing For-Hire Vehicle (FHV) trajectories with different orders and updates the behavioral strategies through interactive feedback with the environment. We implement and evaluate the proposed method with a real-world trajectory dataset, demonstrating that LAMD$^{2}$outperforms all the baselines and reduces the fleet size and energy consumption by the average of 2.93% and 8.01%, respectively, compared to the real-world systems.

Green Light Optimized Speed Advisory (GLOSA) systems have emerged to allow drivers to pass traffic lights during a green interval. However, various adaptive and intelligent traffic light control approaches have been adopted in many cities, resulting in the development of current GLOSA technologies lagging behind that of traffic light technologies. When taking diverse dynamic traffic lights into account, it is difficult to model the interactions between vehicles and traffic lights, which is further exacerbated by the hybrid control strategies of traffic lights. To this end, we design a new GLOSA systemSpeedAdvto provide optimal speed advisory for addressing diverse traffic lights. We formulate the problem as a Multi-Agent Markov Decision Process (MAMDP) with an implicit common goal and propose a heterogeneous-agent collaborative framework based on reinforcement learning. Three main modules are used in the system: i) a spatio-temporal relation reasoning module based on the phase-aware attention mechanism pays more attention to the traffic rules and traffic flow diversion of adjacent intersections to predict traffic conditions for a few seconds later; ii) a behavior approximating module based on imitation learning is introduced to approximate the phases of diverse traffic lights; iii) a speed advisory module provides the optimal speed advisory based on policy gradient reinforcement learning relying on the above two modules and other information collected by vehicles. We implement and evaluateSpeedAdvwith a real-world trajectory dataset, together with a field test based on a prototype system, demonstrating thatSpeedAdvimproves the overall performance by at least 24.1% in terms of travel time, energy consumption, safety, and comfort compared to the state-of-the-artGreenDrivemethod.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps bandwidth to transmit volumetric videos, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-calledexplicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This paper introduces a new concept calledimplicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments on public datasets show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.64× and 3.34× improvement compared with two state-of-the-art baselines, respectively.

Deploying deep learning models to edge devices for low-latency and privacy-preserving applications has become a trend. To adapt to heterogeneous devices and data, it is significant to generate customized models. However, existing model adaptation approaches require edge devices to make interactions (collecting hardware information or local data) with the cloud, which raises privacy concerns, increases communication costs, and burdens the cloud. By contrast, we proposeCamoNet, a universal on-device model adaptation framework with zero interaction between devices and the cloud. InCamoNet, a lightweight on-device neural architecture search module is utilized to quickly generate a customized model for subsequent on-device training, followed by an on-device contrastive transfer learning module to effectively leverage unlabeled data for fine-tuning the customized model. Extensive experimental results show thatCamoNetcan effectively run on various edge devices. Compared with the SOTA model adaptation approaches,CamoNetachieves significant accuracy improvement by 25.2% on average for image classification, 10.1% on average for object detection, and reduces the training memory by 4.8-11.4×. We will open-source our models and tools for edge AI developers.

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, i.e., adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95$^{\rm \textit {th}}$percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose TrafAda, a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed TrafAda on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that TrafAda improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.

Crowd logistics, as an emerging delivery paradigm, provides a cost-efficient way of leveraging crowdsourcing couriers to help express enterprises to match the surging delivery demands that are hard to be addressed by regular couriers only during online mega sale days. However, it is a challenging problem how to recruit an appropriate number of crowdsourcing couriers and assign an appropriate number of parcels to them and regular couriers, as many practical issues need to be considered, such as the dynamic competitive crowdsourcing market, the turnover of crowdsourcing couriers, and unique workload patterns of regular couriers. We design a crowdsourcing-assisted express system called COME to coordinate crowdsourcing and regular couriers for minimizing the overall cost of labor payment and parcel backlog. In COME, we design an Opponent-Aware Reinforcement Learning model to learn the recruitment difficulty in a competitive crowdsourcing market to make an appropriate recruitment plan, and design a four-staged approach to make an appropriate parcel assignment plan, which can address not only the dynamic recruitment difficulty but also the dynamic number of couriers. We have implemented and deployed COME on a real-world crowdsourcing-assisted express system in China involving 1358 delivery stations over 145 cities, and extensively evaluated it with a four-year real-world dataset, demonstrating its great advantage over other alternative solutions and showing high feasibility and generality.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-called explicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This work introduces a new concept called implicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.37x improvement compared with two baselines.

Traffic prediction under various conditions is an important but challenging task. Latest studies have achieved promising results but suffer degraded performance without exception under abnormal conditions (e.g., accidents), as the traffic patterns under abnormal conditions often deviate from the normal seriously. To adapt to both normal and abnormal conditions, we propose theMulti-taskMulti-rangeMulti-subgraphAttentionNetwork (M3AN), a novel deep learning model to explicitly model the impacts of abnormal events for condition-aware traffic prediction. It constructs different subgraphs to model node features to address the abrupt traffic patterns with sparse abnormal event data, and uses an attention mechanism to capture dynamic spatial dependencies. Meanwhile, a multi-task fusion module is built upon a road-segment graph and an intersection graph to enhance the ability of capturing complicated dependencies, together with a multi-range attention module for automatically learning the influences of abnormal events with lower computational complexity. Experimental results on two real-world traffic datasets show that our M3AN outperforms state-of-the-art approaches under both normal and abnormal conditions.

For a new city that is committed to promoting Electric Vehicles (EVs), it is significant to plan the public charging infrastructure where charging demands are high. However, it is difficult to predict charging demands before the actual deployment of EV chargers for lack of operational data, resulting in a deadlock. A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city. However, the demand prediction and charger planning depend on each other, and it is required to re-train the prediction model to eliminate the negative transfer between cities for each varied charger plan, leading to the unacceptable time complexity. To this end, we design an effective solution of S imultaneous Demand P rediction A nd P lanning ( SPAP ): discriminative features are extracted from multi-source data, and fed into an Attention-based Spatial-Temporal City Domain Adaptation Network ( AST-CDAN ) for cross-city demand prediction; a novel Transfer Iterative Optimization ( TIO ) algorithm is designed for charger planning by iteratively utilizing AST-CDAN and a charger plan fine-tuning algorithm. Extensive experiments on real-world datasets collected from three cities in China validate the effectiveness and efficiency of SPAP . Specially, SPAP improves at most 72.5% revenue compared with the real-world charger deployment.

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we proposeLEVID, aLEarning-based cooperativeVehIcle-roaDscheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.

Object detection with camera has achieved promising results using deep learning methods, but it suffers degraded performance under adverse conditions (e.g., foggy weather, poor illumination). To remedy this, some recent studies resort to leveraging the complementary mmWave radar, which is less affected by adverse conditions, and designing effective fusion methods. However, the existing early fusion methods are vulnerable to data noise, while the existing late fusion methods ignore the association of object information between feature maps in the early stage. To overcome these shortcomings, we propose a Global-Local Feature Enhancement Network (GLE-Net), a two-stage deep fusion detector, which first generates anchors from two sensors and uses an auxiliary module to locally enhance the single-branch missing proposals, and then fuses the global features from the multimodal sensors to improve final detection results. We collect two datasets under foggy weather and poor illumination conditions with diverse scenes, and conduct extensive experiments, verifying that the proposed GLE-Net surpasses other state-of-the-art methods in terms of Average Precision (AP).

Energy and cost are two primary concerns when leveraging drones for urban sensing. With the advances of wireless charging technologies and the inspiration from the sparse crowdsensing paradigm, this paper proposes a novel drone-based collaborative sparse-sensing framework DroneSense, demonstrating its feasibility for sustainable urban-scale sensing. We focus on a typical use case, i.e., leveraging DroneSense to sense open parking spaces. DroneSense selects a minimum number of Points of Interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement. However, drone-based sensing is different from human-centric crowdsensing, resulting in a series of new problems, including which POIs are visited first, when and where to charge drones, which drones to charge first, how much to charge, and when to stop the scheduling. To this end, we design a holistic solution, including context-aware matrix factorization for parking occupancy data inference, progressive determination of task quantity, deep reinforcement learning (DRL) based task selection, energy-aware DRL-based task scheduling, and adaptive charger scheduling. Extensive experiments with a real-world on-street parking dataset from Shenzhen, China demonstrate the obvious advantages of DroneSense.

Multistep service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial–temporal dependencies, such as interstation semantic dependencies, interline spatial dependencies, and interservice temporal dependencies, which are not effectively modeled by existing methods. To address these challenges, we propose a spatiotemporal hashing multigraph convolution network, called ST-HMGCN. ST-HMGCN constructs two types of subgraphs from perspectives of physical adjacency and semantic similarity to explicitly capture spatial–temporal dependencies among bus stations/lines, and integrates the interservice temporal correlations to achieve the service-level bus passenger flow forecasting. Moreover, it utilizes the hashing graph convolution to extract the dynamic spatial correlations among graph nodes. Furthermore, a temporal-attention block with residual connections is used to model the nonlinear temporal correlations between different time intervals of each station, which significantly reduces the error propagation among prediction time steps. Finally, we use a large-scale real bus operation data set to conduct an extensive evaluation of ST-HMGCN and 11 state-of-the-art baselines, and further leverage the passenger prediction results of our model to provide crowdedness-aware route recommendation. The experimental results verify the effectiveness of the proposed modeling method and its application value in intelligent transportation.

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.

Reliable traffic flow prediction is of great value in the field of transportation, which, for example, contributes to traffic control and public safety. The key of achieving better performance is to well capture the non-linear spatial-temporal dependency. The state-of-the-art works consider both aspects, but they ignore the effect of the global trend on local dynamics and fail to capture long-term dynamic dependencies. In this article, we propose a novel Global-Local Temporal Convolutional Network (GL-TCN) to break through these limitations. Specifically, a novel local temporal convolutional mechanism is proposed to capture the long-term local dynamics effectively. Meanwhile, the global and local flow patterns are integrated to handle the effect of the global flow trend on local dynamics. To the best of our knowledge, this is the first work to utilize the temporal convolutional network for traffic flow prediction. Experiments on two real-world datasets demonstrate the superior performance of our method over several state-of-the-art baselines.

Mobile crowdsensing (MCS) and unmanned vehicle sensing (UVS) provide two complementary paradigms for large-scale urban sensing. Generally, MCS has a lower cost but often confronts sensing imbalance and even blind areas due to the limitation of human mobility, whereas UVS is often capable of completing more demanding tasks at the expense of limited energy supply and hardware cost. Thus, it is significant to investigate whether we could integrate the two paradigms for high-quality urban sensing in a cost-effective collaborative way. However, it is nontrivial due to complex and long-term optimization objectives, uncontrolled dynamics, and a large number of heterogeneous agents. To address the collaborative sensing problem, we propose an actor-critic-based heterogeneous collaborative reinforcement learning (HCRL) algorithm, which leverages several key ideas: local observation to handle expanded state space and extract the states of neighbor nodes, generalized model to avoid environment nonstationarity and ensure the scalability and stability of network, and proximal policy optimization to prevent the destructively large policy updates. Extensive simulations based on a mobility model and a realistic trace data set are conducted to confirm that HCRL outperforms the state-of-the-art baselines.

Mobile CrowdSensing (MCS) is a new paradigm that leverages pervasive mobile devices to efficiently collect the big sensory data, enabling various large-scale applications. However, people's concerns about the loss of individual privacy seriously hinder the prevalence of MCS applications. Differential privacy is widely focused owing to its rigorous definition and strong privacy guarantee, but the state-of-the-art studies still demonstrate its weakness on correlated data, resulting in compromising individual privacy. In this paper, we investigate the influence of sensing data correlation on differential privacy protection for MCS systems, and explore the perturbation mechanisms from two different perspectives. From a protector's perspective, based on the Bayesian Network to model the probabilistic relationship among sensing data, we use the classical definition of differential privacy to deduce the scale parameter, and present one perturbation mechanism. From an adversary's perspective, based on the Gaussian correlation model to describe the data correlation, we analyze the importance of the maximum correlated group to compute the Bayesian differential privacy leakage, and then provide another perturbation mechanism. Compared with the existing solutions, our mechanisms are applicable to arbitrary aggregate query function, and can avoid introducing too much noise. Moreover, we demonstrate the effectiveness of our mechanisms through extensive simulations.

Mobility models, which reproduce traces with basic crowd mobility patterns, are crucial for realistic mobile network simulation and performance evaluation of the planning strategies used for urban networks (such as transit network, communication network, and crowdsensing network). However, trajectories generated by traditional models are often perceived as not realistic for urban context or lack of scalability and universality. This article presents a data-driven trajectory generating technique, named as TraG, that produces synthetic trajectories with the help of some real-world trajectories. Our technique can automatically extract the context features and statistical mobility features, which characterize the mobility of a specific urban crowd from the input empirical traces and, then, regenerate more trajectories based on demand. Moreover, we also summarize the power-law scale correlation of crowd mobility based on four real-world open datasets, including public bicycle traces in New York City and Washington, D.C., taxicab traces in San Francisco and Shenzhen. Finally, we validate the proposed TraG model via the continuous San Francisco taxicab traces, and the result demonstrates that the trajectories simulated by TraG not only inherit the fundamental statistical features of crowd mobility from real traces, but also reflect the features of urban context.

Bus services play a crucial role in urban transit. It is significant to achieve the fine-grained service-level passenger flow prediction (SPFP), namely to predict the total number of passengers for each service of each bus line passing through each station during the next short-term interval. However, it faces great challenges due to complex factors including inter-station and inter-line spatial dependencies, intra-station and inter-service temporal dependencies, and internal/external influences. To address these challenges, we propose a multitask deep-learning (MDL) approach, calledMDL-SPFP, to jointly predict the arriving bus service flow, line-level on-board passenger flow and line-level boarding/alighting passenger flow by leveraging well-designed deep neural networks calledARM. The MDL framework can mutually reinforce the prediction of each type of flow, and finally integrate the outputs to achieve the fine-grained service-level prediction. The ARM network combines three modules, Attention mechanism, Residual block and Multi-scale convolution, to well capture various complex non-linear spatio-temporal dependencies and influence factors. Extensive experiments based on a large-scale realistic bus operation dataset are conducted to confirm that our MDL-SPFP approach outperforms 10 state-of-the-art baselines, and improves 22.39% accuracy than the best baseline.

Incentive mechanism design has aroused extensive attention for crowdsourcing applications in recent years. Most research assumes that participants are already in the system and aware of the existence of crowdsourcing tasks. Whereas in real-life scenarios without this assumption, it is more effective to leverage incentive tree mechanisms that incentivize both users' direct contributions and solicitations to other users. Although such mechanisms have been investigated, we are the first to propose budget-balanced incentive tree mechanisms, called generalized lottrees, which require the total payout to be equal to the announced budget, while guaranteeing several desirable properties including continuing contribution incentive, continuing solicitation incentive, value proportional to contribution, unprofitable solicitor bypassing, and unprofitable Sybil attack. Moreover, three types of generalized lottree mechanisms, 1-Pachira, K-Pachira and Sharing-Pachira, are presented for supporting diversified requirements. A solid theoretical guideline on the mechanism selection is provided based on the Cumulative Prospect Theory. Both extensive simulations and realistic experiments with 82 users are conducted to confirm our theoretical analysis.

Nowadays we are witnessing an explosive growth of mobile data traffic, but users still often experience insufficient or unstable network bandwidth in many realistic scenarios. Unmanned aerial vehicle mounted base stations (UAV-BSs) provide a novel and promising solution for serving regions with bandwidth shortfall. It is significant to investigate how to deploy UAVs for maximizing the sum throughput of a set of clients scattered in various locations in an effective and efficient way. A basic idea is to use RF ray tracing simulations as a hint to narrow down the search space of UAVs for conducting measurements. Furthermore, we study two key sub-problems, chunk selection, which finds an optimal subset of chunks in the region as the search space of UAVs, and chunk search, which plans the scanning path to cover the search space with the min-max time consumption required for all UAVs. They are both proved to be NP-hard, and heuristic algorithms are proposed to solve them efficiently. A prototype system, UAV-Net, is implemented to conduct measurements by a UAV mounted WiFi AP communicating with 7 clients scattered in a campus, and extensive simulations are combined, reporting an obvious throughput gain with a small measurement overhead and time consumption.

Unmanned Aerial Vehicle (UAV) provides an effective way to create an Aerial Panoramic Map (APM). Generally, it consists of three steps: 1) select a set of locations from a map, 2) take photos from different angles at each selected location one by one, and 3) make panoramic images by the Panoramic Mosaic technology, and then create an APM with these images. However, it is always labor-intensive and time-consuming to complete these steps in a large region such as a campus and a park, due to multiple reasons: 1) inexperience for location selection, 2) low-efficiency for manually operating UAV to fly among different locations one by one and make photos from different angles, and 3) limited energy supply and low-efficiency for a single UAV. DJI GO[1] has been developed to simplify a part of operations in steps 2) and 3), by which we only need one button to take photos automatically from different angles at a selected location. However, it is still required to manually select locations and control UAV to fly among different locations. Moreover, the defects of using a single UAV still exist. By contrast, we aim to design an Auto nomously C ooperative U AV system platform for P anoramic map generation, AutoCUP, which leverages multiple UAVs to full-automatically and high-efficiently complete all steps of creating an APM.

Sweep coverage uses mobile nodes for monitoring Points of Interests (PoIs) in a sensing field. With sweep coverage, information can be effectively gathered without using a lot of static sensors. In this paper, we study the sweep coverage problem with sensing and transmission delay constraints, which is regarded as the Timely Sweep Coverage problem. Specifically, we investigate how to use the minimum number of mobile nodes to cover all PoIs under the two delay constraints. We propose two heuristic algorithms, MR-MinExpand and CoTSweep, to provide timely sweep coverage under different scenarios. MR-MinExpand leverages the difference between sensing and transmission delay constraints to schedule the route for each mobile node. CoTSweep considers the scenario where some PoIs cannot be covered by a single mobile node, and leverages the collaboration between mobile nodes to enable the sink node to collect data from remote PoIs. Extensive simulations and comparisons with previous works are conducted to validate the advantages of our algorithms.

With the explosive growth of mobile devices such as smartphones, it is convenient for participants to perform mobile crowd sensing (MCS) tasks. It is a useful way to recruit participants to perform location-dependent tasks. We first propose Min-Max Task (MMT) planning problem in MCS systems, considering time-sensitivity and heterogeneity of sensing tasks. In other words, how to design a cooperation scheme, in which the participants spend as little time as possible. Then, to address MMT problem, we propose a Memetic based Bidirectional General Variable Neighborhood (MBGVN) algorithm, in which all tasks are separated into groups and traveling path is designed for each participant. Finally, extensive experiments are conducted to demonstrate the benefits of our scheme, outperforming other similar state-of-the-art algorithms.

Crowdsourcing provides a novel and effective way of constructing a location image database for mobile visual location recognition. Compared with traditional location image databases, a crowdsourced database has richer information for location images, with various angles, times, distances and weathers, providing great potential for high recognition accuracy. However, it is inevitable to have various disturbances on these location images, hindering the potential. To address this challenge, we first propose a Common Crucial Feature (CCF) detection algorithm to exclude unimportant visual features from crucial features. To achieve a good balance between the efficiency and accuracy, we further propose a CCF based Visual Hash Bits (VHB) scheme to encode CCF features into hash bits to vote for most matching images. Extensive experiments are conducted on a crowdsourced dataset with 9,064 location images, demonstrating that our scheme outperforms other state-of-the-art schemes.

Unmanned Aerial Vehicles (UAVs) have been widely used in various applications such as inspection, security surveillance, and aerial photography, in which the cooperation of multiple UAVs is significantly important for better accomplishing complex tasks due to the limited capability for individual UAV s. Task planning is the primary issue for the cooperation of multiple UAV s, and has attracted extensive research interests. However, most research fails to account adequately for limited energy on each UAV, which involves in many factors such as different operations for performing a task and various movement patterns besides the distance and turns that have been commonly considered. By contrast, we conduct a series of experiments to obtain the energy model of UAV s. Furthermore, we focus on the energy-efficient min-max task planning (E2M2TP) problem by considering the heterogeneity of tasks and integrating various energy factors, which is beneficial for balancing the workload and energy consumption among UAV s and thus reducing the number of required UAVs. We show that E2M2TP is NP-hard, and propose an energy-aware variable neighbor search (EVNS) algorithm to iteratively optimize both task allocation and path planning. Extensive simulations are conducted to validate that EVNS outperforms the other state-of-the-art algorithms.

The performance of mobile networks is significantly influenced by the mobility patterns of wireless device holders. Human mobility models, which yields synthetic trajectories with essential mobility patterns of crowd, are important for the research and development of mobile networks. However, traditional models are often perceived as not realistic for depicting urban context (such as urban hotspots and direction of crowd flow). This paper proposes a temporal correlation based model that produce large-scale of synthetic trajectories on the basis of a short period of time real human traces. The trajectories generated by our model not only maintain the inherent statistical features of human mobility, but also “learn” the urban context from real traces. For simulating crowd mobility in a city, our model first extract the statistical features and urban context features from the the real traces, then regenerate synthetic trajectories on the basis of temporal correlation of human mobility. We validate our model and findings through three open datasets: taxicabs' traces in San Francisco, public bicycles' traces in Washington D.C. and New York City.

Recognizing object location by taking a photo with smartphones is useful for many location-based services. However, start-of-the-art technologies for both localization and location recognition have difficulty in achieving satisfactory performance. Moreover, it is a challenging issue to construct and maintain a large-scale image database for existing visual-based location recognition systems. To cope with these issues, we introduce CrowdOLR, a crowdsourcing based object location recognition system, which collects one location image together with various rich sensory data (GPS coordinates, azimuth angle, tilt angle, etc.) as a fingerprint of a location query and matches it to a fingerprint database crowdsourced from users' smartphones. We designed a simple and efficient user action mode and proposed a series of fingerprint extracting, searching, and matching methods, so that CrowdOLR satisfies five desirable properties: high recognition accuracy, user friendliness, quick response, no/little site survey, and timely update. We implemented CrowdOLR and collected 8100 location fingerprints of 162 objects for performance evaluation. Extensive experiments demonstrate that CrowdOLR achieves promising results in various complicated and realistic scenarios.

Participatory sensing systems can gather and process personal data to produce some knowledge about the world. In this setting, sensing data quality has been the primary focus of widespread adoption of many systems. To encourage more participants to contribute high-quality data, many incentive mechanisms have been proposed to motivate them. Unfortunately, this brings about some other new issues. Firstly, the application server must have to analyze and process a large-scale of sensing data for the incentives, but most of them might not satisfy the essential data requirements. Moreover, the application server cannot determine whether those important data originate from the legal participants, thus has to check at the expense of efficiency loss. Even worse, since anyone who knows the sensing task can report sensing data, the application server would face the denial-of-service attacks from malicious participants. In this paper, we propose a Quality-aware Attribute-based Filtering (QAFilter) scheme to address these problems. In QAFilter, the application server can distribute a Receiver Filtering Policy along with the sensing task, so that only the participants whose data quality satisfies the policy can report sensing data to the server. Furthermore, in order to maintain the data confidentiality, the participant can encrypt the sensing data under his requirement such that only the receivers who hold the related attributes can decrypt it. In particular, the costs of computation and communication are constant and independent of the number of required data attributes, thus making our scheme more adapt to personal mobile devices and the wireless network environment. Our security analysis and experiment evaluation demonstrate the presented scheme’s security strength and performance efficiency, respectively.

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.

Mobile crowd sensing (MCS) is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous novel applications. To achieve good service quality for an MCS application, incentive mechanisms are necessary to attract more user participation. Most existing mechanisms apply only for the offline scenario where all users report their strategic types in advance. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arriving, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a nonnegative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.

Mobile crowd-sensing applications produce useful knowledge of the surrounding environment, which makes our life more predictable. However, these applications often require people to contribute, consciously or unconsciously, location-related data for analysis, and this gravely encroaches users' location privacy. Aggregate processing is a feasible way for preserving user privacy to some extent, and based on the mode, some privacy-preserving schemes have been proposed. However, existing schemes still cannot guarantee users' location privacy in the scenarios with low density participants. Meanwhile, user accountability also needs to be considered comprehensively to protect the system from malicious users. In this paper, we propose a participant-density-aware privacy-preserving aggregate statistics scheme for mobile crowd-sensing applications. In our scheme, we make use of multi-pseudonym mechanism to overcome the vulnerability due to low participant density. To further handle sybil attacks, based on the Paillier cryptosystem and non-interactive zero-knowledge verification, we advance and improve our solution framework, which also covers the problem of user accountability. Finally, the theoretical analysis indicates that our scheme achieves the desired properties, and the performance experiments demonstrate that our scheme can achieve a balance among accuracy, privacy-protection and computational overhead.

With the rapid advancements of mobile computing, mobile location recognition is becoming an important and useful service, which recognizes the logical locations of places/scenes that users are interested in, instead of physical coordinates. Most of the existing mobile location recognition systems utilize the image as visual fingerprint of a place, and need to construct a large-scale visual fingerprint database in advance. However, collecting visual fingerprints is a labor-intensive and time-consuming procedure. In order to address this problem, we propose a novel crowdsourcing-based framework, and leverage a variety of sensors embedded in smartphones to collect richer location fingerprints for exploring their positive effects. To achieve higher recognition accuracy, we propose an object-centric fingerprint searching which can sufficiently take advantage of smartphone sensors and determine more accurate searching space than the traditional user-centric method. We build a crowdsourcing-based database with richer fingerprints and implement a location recognition system, called CrowdLR. Extensive experiments verify that our object-centric method can achieve promising results maintaining around 10% precision higher than the user-centric method.

The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. Urban resolution describes how sensitivity the urban sensing system could achieve for environment monitoring applications. Then, we study the relationship between resolution r and number of sensing nodes s, and reveal the linear growth relationship between √r and √s . Furthermore, by employing a commonly used human/vehicle mobility model, SLAW, we find that the distribution model of urban sensing nodes is able to be described by a truncated Pareto distribution, and derive the complementary cumulative distribution function (CCDF) of urban resolution. The CCDF reveals the radio of the sub-regions which satisfy the required sensing quality to the whole region. Our findings provide valuable insights to infer the urban sensing quality according to the scale of urban sensing system or determine how many smartphone/vehicles needed for participating in urban sensing applications. Finally, based on five real datasets-three human/vehicle trajectory datasets and two environment monitoring datasets, we examine the metric of urban resolution and evaluate the main results in this paper.

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for building various sensing maps. Most of existing mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, this approach cannot be applied in the scenarios with poor network coverage or expensive network access. Existing data forwarding schemes for mobile opportunistic networks are not sufficient for sensing applications as spatial-temporal correlation among sensory data has not been explored. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated independently, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. Both the theoretic analysis and simulation results show that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We also evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93 percent compared with the non-cooperative scheme; ERF can reduce the transmission overhead by 78 percent compared with Epidemic Routing (ER); BSWF can increase the delivery ratio by 16 percent, and reduce the delivery delay and transmission overhead by 5 and 32 percent respectively, compared with Binary Spray-and-Wait (BSW).

Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.

Opportunistic sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to collect data ubiquitously for large-scale urban sensing. Existing work lacks an in-depth investigation on the sensing quality of such sensing systems, which faces two basic problems: 1) how to measure the sensing quality? and 2) how many humans or vehicles are necessary to satisfy the sensing quality requirement of the whole urban area? To solve the first problem, we propose a metric called Inter-Cover Time (ICT) to characterize the opportunity with which a sub region is covered, which reflects the sensing quality directly. According to the empirical measurement studies on real mobility traces of thousands of taxis collected in Beijing and Shanghai, we find that the aggregated ICT Distribution (ICTD) closely resembles a truncated power-law distribution regardless of the size of sub regions and the number of vehicles. We also analyze the reasons behind this particular pattern by the evaluation on four known mobility models. To solve the second problem, we further propose a metric called opportunistic coverage ratio based on the ICTD to characterize the relationship between the sensing quality of an urban area and vehicle number. Our results provide fundamental guidelines on the measurement of sensing quality and network planning for opportunistic urban sensing applications.

With the popularity and advancements of smartphones, mobile users can sense the city using variety of sensors opportunistically, and forward the sensory data to the monitoring center for building sensing maps through intermittent connections with short-range communications. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated individually, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. We theoretically prove that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93% compared with the non-cooperative scheme, ERF can reduce the transmission overhead by 78% compared with Epidemic Routing (ER), BSWF can increase the delivery ratio by 16%, and reduce the delivery delay and transmission overhead by 5% and 32% respectively, compared with Binary Spray-and-Wait (BSW).

Mobile sensors are a viable choice for providing monitoring service on a set of Points of Interest (PoIs) in a large sensing field. In some applications, each PoI should be covered periodically (sweep coverage), and the collected data should be delivered to the sink node timely (timely transmission), namely both the sensing and transmission delay constraints for each PoI should be satisfied, which we call as timely sweep coverage. We investigate how to optimize the movement path of one mobile sensor to satisfy the two delay constraints for each PoI, so that the required movement velocity for the mobile sensor is minimized. We consider two cases: 1) all PoIs are placed along a straight line (linear case), and 2) all PoIs are arbitrarily placed on a plane (general 2-D case). Under the linear case, the optimal algorithm is presented. Under the general 2-D case, we prove the problem is NP-hard, and two algorithms, STSP and ITSP, are presented. We prove that the approximation ratio of STSP depends on the ratio between the sensing and transmission delay constraints. The ITSP can improve the solution much especially when the two delay constraints differ greatly for each PoI. Extensive simulation results are provided to evaluate our algorithms.

Probabilistic forwarding methods have been exploited in opportunistic networks to reduce the overhead of epidemic routing. However, most existing methods make all the nodes forward messages with the same probability (i.e., equal scheme), which causes the energy unbalance of nodes. To guarantee the energy balance of nodes and prolong the network lifetime, we design a differentiated scheme, i.e., different nodes are assigned with different forwarding probabilities based on their respective energies. We model the message dissemination based on the differentiated scheme, and formulate two optimization problems: maximize the message deliver probability under the constraint on the total energy consumption, and based on this, maximize the network lifetime under the constraint on the energy consumption of each node. By solving these two optimization problems, we derive the optimal differentiated forwarding probabilities by theoretical analysis. Our simulation results show that our designed differentiated scheme can guarantee the message deliver probability and extend the network lifetime, compared with the equal scheme.

Multimedia sensor networks (MSNs), which allow capturing acoustic and visual information, provide an unprecedented opportunity for variety of applications. This paper investigates the heterogeneous coverage problem in MSNs, i.e., how many multimedia sensors should be deployed to guarantee that each point in the monitored region is covered by multiple types of heterogeneous sensors. It is different from the coverage problem in conventional homogeneous sensor networks, mainly because that it is based on a heterogeneous sensing model, which is a hybrid of the omni-sensing model and the directional-sensing model. We propose a mathematical model to describe the relationships among the number of multimedia sensors, the sensing radius of the acoustic collection module, the sensing radius and the sensing angle of the visual collection module and the heterogeneous coverage rate. Our simulation results show that deploying our model in practical scenarios is effective.

Object/event classification is an important aspect in the applications of Wireless Audio Sensor Networks (WASNs). In order to reduce the computational burdens of individual sensors, distributed classification method is adopted, i.e., the individual sensors only perform binary classification and send out a binary decision to the fusion center. However, it brings about a new coverage problem, namely, how to deploy sensors so that all locations in the surveillance region are covered by multiple types of sensors with different sensing radii for collaborative classification. We call it k-class coverage problem. In this paper, we propose two algorithms: straight-forward algorithm and divide-and-conquer algorithm. The first algorithm converts the problem into k-class set cover problem, and the second algorithm converts the problem into solving set 1-cover problem k times. Greedy strategies are used in both of two algorithms. Performance analysis and simulation results are provided for evaluating two algorithms.

The audio surveillance is traditionally performed by using wired microphones. We propose an alternative surveillance system by using audio sensor networks for event classification in our living environment. We first compare two classical acoustic features - the Fast Fourier Transform (FFT) based acoustic features and the Mel-Frequency Cepstral Coefficient (MFCC) based acoustic features, and then, by using the FFT based acoustic features, we present a hierarchical classification approach for distinguishing abnormal or catastrophic events. A distance based decision fusion is used to combine the sensory information collected by the audio sensors. We present the performance analysis on the proposed approaches using a real audio sensor network.