In warehouse-based e-commerce, accurate category-level warehouse demand prediction is essential to ensure effective inventory management. Existing works mainly explore advanced time series models to capture the temporal dynamics, failing to mine cross-category and cross-warehouse correlations effectively. In this paper, we explore large language models to understand the semantic information and fuse multi-view knowledge to enhance demand prediction. However, it is not trivial due to: i) the inaccurate LLM’s understanding of the category-related and warehouse-related textual input; and ii) the complicated cross-warehouse knowledge utilization. To solve the above challenges, we propose an LLM-guided multi-task graph learning framework, LMGL-WD, for category-level warehouse demand prediction. Specifically, LMGL-WD includes three components: i) an LLM-guided category series encoding module to represent each category through contextual and series embedding; ii) a cross-warehouse category learning module to adaptively mine the informative knowledge from cross warehouses to enhance category representation; and iii) a cross-category multi-task learning module to adaptively capture cross-category correlations to improve demand prediction. Extensive evaluation results with real-world data collected from one of the largest e-commerce platforms in China demonstrate that LMGL-WD achieves superior performance, e.g., reduces MAPE by up to 31.59%, compared to state-of-the-art methods.

The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essential form of passive data management. Multimodal sequential recommendation, which leverages diverse item information such as text and images, has shown great promise in enriching item representations and deepening the understanding of user interests. However, most existing models rely on heuristic fusion strategies that fail to capture the dynamic and context-sensitive nature of user-modal interactions. In real-world scenarios, user preferences for modalities vary not only across individuals but also within the same user across different items or categories. Moreover, the synergistic effects between modalities-where combined signals trigger user interest in ways isolated modalities cannot-remain largely underexplored. To this end, we propose CAMMSR, a Category-guided Attentive Mixture of Experts model for Multimodal Sequential Recommendation. At its core, CAMMSR introduces a category-guided attentive mixture of experts (CAMoE) module, which learns specialized item representations from multiple perspectives and explicitly models inter-modal synergies. This component dynamically allocates modality weights guided by an auxiliary category prediction task, enabling adaptive fusion of multimodal signals. Additionally, we design a modality swap contrastive learning task to enhance cross-modal representation alignment through sequence-level augmentation. Extensive experiments on four public datasets demonstrate that CAMMSR consistently outperforms state-of-the-art baselines, validating its effectiveness in achieving adaptive, synergistic, and user-centric multimodal sequential recommendation.

The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the availability of high-quantity and high-accuracy datasets, which are yet difficult to obtain in large deformation problems. During the dataset construction process, a dilemma stands between data quantity and data accuracy, leading to suboptimal performance in the DL models. To address this challenge, we focus on a representative application of large deformations, the stretch bending problem, and propose FilDeep, a Fidelity-based Deep Learning framework for large Deformation of elastic-plastic solids. Our FilDeep aims to resolve the quantity-accuracy dilemma by simultaneously training with both low-fidelity and high-fidelity data, where the former provides greater quantity but lower accuracy, while the latter offers higher accuracy but in less quantity. In FilDeep, we provide meticulous designs for the practical large deformation problem. Particularly, we propose attention-enabled cross-fidelity modules to effectively capture long-range physical interactions across MF data. To the best of our knowledge, our FilDeep presents the first DL framework for large deformation problems using MF data. Extensive experiments demonstrate that our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing.

Tracking-based social behavior analysis of lab animals, especially mice, is crucial for research in biology, medicine, and psychology. However, existing visual tracking systems struggle to maintain long-term, accurate tracking due to frequent identity association errors, which lead to extensive manual correction and limit research scalability. This paper introduces mTrack, an RFID–vision hybrid mouse tracking system that leverages the precise identification capability of UHF RFID to assist the visual tracker, enabling long-term, self-correcting, and high-accuracy mouse tracking. Our experiments demonstrate that mTrack can track up to ten mice simultaneously with over 99.23% identification accuracy and a 0.4 cm 99th-percentile localization error, reducing error rate by more than 40x compared with visual tracking systems. Our field studies indicate that mTrack can sustain this performance for over two hours and be seamlessly integrated into existing animal behavior research workflows. The code and dataset are open-sourced at https://github.com/SOAR-PKU/mTrack.

Quantization-conditioned backdoor attacks, which exploit model quantization states to trigger malicious behavior, pose a hidden threat to deep learning security. However, current studies ignore the feasibility of attacks,i.e., detection visibility, and computational overhead, leading to significant constraints on their practical deployment in real-world adversarial scenarios. To address these limitations, we propose Quantization-conditioned Efficient Stealthy Trojan (QuEST), a novel framework that enhances both the stealth and efficiency of backdoor attacks under quantization constraints. For enhancing stealthiness, we design a stealth-optimized training scheme that benefits from the parametric backdoor injection and trigger scaling augmentation to maintain the consistency of model behavior during attack. In this way, the defender will fail to capture the suspicious behavior differences for detection due to the made efforts in both model-side and data-side. To improve efficiency, we introduce information-guided parameter sharing, which utilizes parameter redundancy analysis and Fisher divergence metrics to identify a minimal amount of quantization-preserved parameters for back-door injection. These parameters are strategically shared between the malicious and benign models, enabling concurrent training and substantially reducing overall training time. Extensive experiments demonstrate that QuEST maintains competitive attack success rates while improving stealth performance by 18.75% and reducing computational costs by 26.66% on average compared to state-of-the-art methods, highlighting QuEST’s potential for more practical adversarial deployments in real-world scenarios. Our code is available here.

The surge in location-based services has driven the demand for accurate indoor localization techniques, with WiFi based localization emerging as a promising solution due to its extensive coverage in indoor environments. This paper presents BFI-L10N, an indoor localization method that leverages beam forming feedback information (BFI) obtained from standard multi-user multiple-input multiple-output (MU-MIMO) WiFi operations. Unlike traditional channel state information (CSI) based methods that require vendor-specific firmware patches or restricted device support, BFI leverages standardized MU-MIMO operations, enabling compatibility with off-the-shelf WiFi devices. BFI-L10N processes the BFI data collected during the beam forming process through a deep learning framework and uses a BERT model for localization. Compared to CSI-based systems, BFI-L10N offers advantages such as reduced overhead, enhanced sensitivity, compatibility with standard devices, and real-time predictions. Our experimental results in two distinct indoor environments demonstrate that BFI-L10N achieves average localization accuracies of 10.7 cm and 15.5 cm in a research laboratory and a conference room, respectively, outperforming the state-of the-art CSI techniques by 28%. Moreover, the BERT model can be fine-tuned after pre-training across multiple locations, which enhances the versatility of BFI-L10N. This paper presents a novel perspective on WiFi sensing and lays the foundation for practical indoor localization using standard WiFi infrastructure.

Spatial Crowdsourcing (SC) has become a popular paradigm for executing spatiotemporal tasks, such as urban sensing, ride-hailing, and last-mile delivery. In many of these service applications, it is common for workers to undertake multiple tasks simultaneously, as this allows them to efficiently complete tasks with a convenient route. In multi-task SC, location privacy, movement cost, and service quality constitute the three most critical factors. However, the inherent contradictions among these factors pose significant challenges in simultaneously ensuring them all, and to date, no existing work has addressed all three factors concurrently. To fill the research gap, in this paper, we propose a Privacy-Preserving, Cost-Aware, and Quality-Enabled Service Planning (PCQ-SP) scheme for multi-task SC. First, we introduce a secure distance acquisition method with additive homomorphic encryption and garbled circuits, which enables the calculation of distances between workers and tasks while preserving both their location privacy. Second, we design a Thompson Sampling-based quality evaluation method to assess each worker's service quality by balancing exploration and exploitation. Third, we propose a minimum-cost flow-driven service planning method to derive the optimized service plans, which aims for high quality and low cost while preserving location privacy. To our knowledge, our PCQ-SP scheme is the first to simultaneously consider location privacy, movement cost, and service quality in SC systems. Extensive evaluations demonstrate the effectiveness of our PCQ-SP scheme in enhancing service quality, reducing movement costs, and ensuring location privacy.

Wi-Fi is the de facto standard for providing wireless access to the Internet using the 2.4GHz ISM (Industrial Scientific Medical) band. LoRa (Long Range) is specially designed for Low-Power, Wide-Area Networks (LPWANs) and has a broad range of applications in Internet of Things. Tens of billions of mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this challenge, we propose a method that enables LoRa devices to establish connections and engage in communication with Wi-Fi networks. A key observation of this study is that when a LoRa frame collides with an ongoing Wi-Fi transmission, the Wi-Fi receiver captures and retains the LoRa data. By analyzing the decoded Wi-Fi payload, we can retrieve the LoRa data, and this method remains fully compatible with existing commodity Wi-Fi hardware. Moreover, evaluations with Universal Software Radio Peripheral (USRP) and commodity devices demonstrate reliable wireless communication from LoRa to Wi-Fi networks with a high reliability in frame reception and low frame error rates across various indoor and outdoor environments.

Points of Interest (POIs) are crucial for urban planning and location-based services. However, the high dynamics and expensive updating costs of POIs pose a key roadblock for their urban applications. This is especially true for developing countries, where active economic activities lead to frequent POI updates (e.g., merchants closing down and new ones opening). Therefore, POI updating, i.e., detecting new POIs and different names of the same POIs (alias) to update the POI database, has become an urgent but challenging problem to address. In this paper, we attempt to answer the research question of how to detect and update large-scale POIs via a low-cost approach. To do so, we propose a novel mobile sensing framework called UrbanPOI, which consists of three key modules: (i) a hierarchical POI candidate generation module based on the POINet model that detects POIs from shipping addresses; (ii) a new POI detection module based on the Siamese Attention Network that models multi-modal data and crowd intelligence; and (iii) a POI profiling model to infer the semantic category and the spatial location of updated POIs. Extensive results on real-world logistics delivery datasets show that our model outperforms state-of-theart models in Beijing City by 26.2% in precision and 10.7

Last-mile delivery from delivery stations to customers is now mainly finished by dedicated couriers. In practice, each courier generally collects orders destined for one delivery area at the station and delivers them. However, the long distance between the station and the delivery area, due to practical reasons such as the expensive station rental fee in the downtown area, increases the travel time and decreases delivery efficiency. We solve the problem with relay transportation, where one or multiple relay couriers collect orders at the station and send them to delivery couriers, and delivery couriers focus on order delivery in the corresponding areas. We design a mobility-aware single relay courier scheduling system calledREDEto minimize the average relay order delivery time (ARODT) for last-mile delivery. First, a heterogeneous task aware route prediction algorithm is proposed to characterize the delivery courier's mobility. After that, a distance-aware greedy algorithm and an ARODT-constrained exchange algorithm are designed to generate the relay route. A hybrid last-mile delivery systemREDE+is then designed to further reduce ARODT ofREDE. We also proposeREDE-COto optimize multiple relay courier cooperative scheduling for order relay. Extensive evaluation shows the effectiveness ofREDE,REDE+, andREDE-COin reducing ARODT.

In last-mile delivery, efficient courier scheduling for heterogeneous tasks (i.e., delivery, pick-up, and customer expansion tasks) simultaneously benefits customers, couriers, and the platform. Existing scheduling methods for heterogeneous tasks neglect long-term optimization or are based on simple agent mobility settings. In this work, we focus on a scenario with varying task characteristics compared to existing studies and explore reinforcement learning for courier scheduling in last-mile delivery, considering fine-grained courier mobility. It is challenging due to: i) the large state and action space; ii) the courier cooperation under asynchronous decision-making caused by various task service times and courier travel times between location pairs; and iii) the multi-objective optimization under the dynamic environment. Therefore, we propose a heterogeneous task-aware cooperative courier scheduling system,HeroCS, to improve courier working efficiency and fairness in last-mile delivery. Specifically, we first design a distance-constrained courier modeling module to generate the single courier scheduling decisions, where a distance-aware top-$k$action pruning scheme is proposed to reduce state representation and action space. Then, a cooperative scheduling learning module is explored for optimizing courier cooperation. A status-aware action masking scheme is designed to solve asynchronous decision-making in model training. We also design a hybrid reward function to optimize various objectives adaptively. Extensive evaluation with real-world data from one of the largest logistics companies in China demonstrates that compared to state-of-the-art methods,HeroCSimproves the task completion rate by up to 40.7% and reduces courier unfairness by up to 83.3%.

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes aQuality-awareDual-BilateralPrivacyPreserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

Logistics platforms increasingly offer real-time door-to-door order pickup services to enhance customer convenience. However, a high volume of unexpected order cancellations negatively impacts both customer experience and logistics profitability. Accurately identifying whether these cancellations stem from customers' decisions or couriers' non-compliant behaviors is essential for implementing targeted operational improvements. Simply interpreting customer-courier dialogues, however, risks being one-sided. The primary challenge lies in adaptively correlating dialogue content with the varying behaviors of couriers. To address this, we developCOCO, a cause identification framework for order cancellation in logistics that consists of: i)Multi-modal features exploration, which analyzes dialogues and couriers' behaviors (both historical and current); ii)Multi-modal features aggregation, which uses a hierarchical attention mechanism to adaptively capture the dynamic correlations within dialogues and behaviors; iii)LLM-enhanced refinement, which leverages Large Language Models to accurately process a large number of unlabeled dialogues, significantly enhancingCOCO's generalization and performance. To further enhanceCOCO's effectiveness, we introduceCOCO+, which incorporates customers' behaviors to better identify customer-related causes. Our extensive evaluation with data collected from JD Logistics demonstratesCOCO+'s exceptional performance, achieving an 12.2% increase in precision and a 9.1% improvement in recall over existing methods. Furthermore, after deployingCOCO+in real-world scenarios, we observed an adoption rate of 89.5% and improved on-time pickups even under high-demand conditions, as confirmed through A/B testing.

Accurate long-term traffic prediction in Low Earth Orbit (LEO) satellite networks is essential for proactive resource allocation and congestion avoidance, yet remains challenging due to highly dynamic topologies, intermittent connectivity, and scarce real traffic data. Existing approaches are largely limited to short-term prediction or assume static spatial dependencies, making them inadequate for non-stationary LEO environments. To address these challenges, this paper proposes DST-DEformer, a dynamic spatial–temporal Transformer framework that jointly models evolving inter-satellite topology and multi-scale temporal dependencies. Specifically, a topology-adaptive graph convolution module captures time-varying spatial correlations, while a dual temporal encoder decouples long-term global trend modeling from short-term local fluctuation learning. In addition, a hybrid simulation–calibration framework is developed to generate realistic satellite traffic by incorporating orbital dynamics, demographic information, and real-world traffic trends. Extensive experiments on simulated LEO satellite traffic and the PEMS08 benchmark show that DST-DEformer consistently outperforms state-of-the-art methods in long-term prediction, achieving 4%-13% reductions in MSE and MAE and significantly slower error accumulation as the prediction horizon increases. These results demonstrate the effectiveness and robustness of DST-DEformer for long-term traffic prediction under dynamic network topologies.

Cross-Technology communication (CTC) has emerged as a pivotal solution for enabling communication between heterogeneous wireless devices, particularly in scenarios involving high-power and long-range networks. This paper introduces an innovative approach called WeRa, which leverages IEEE 802.11n technology to emulate LoRa waveforms, thus achieving efficient CTC from Wi-Fi to LoRa. WeRa utilizes orthogonal frequency division multiplexing (OFDM) technology in 802.11n to emulate LoRa chirp signals. To address the unique characteristics of LoRa signals, we propose a subcarrier selection scheme to enhance the communication performance between commercial Wi-Fi devices and commercial LoRa devices. During the Wi-Fi emulation process, frequency offsets are inevitable due to technical constraints. To mitigate this, we implement a fixed frequency compensation mechanism for the emulated signals based on thorough analysis. To tackle errors introduced by the cyclic prefix (CP), we build upon the boundary-flipping method proposed in WeBee and introduce a dynamic mode-flipping technique, effectively reducing the interference of CP on the emulated signals. We successfully implemented a prototype of WeRa on commercial Wi-Fi devices, the USRP platform, and commercial LoRa devices. Extensive experiments demonstrate WeRa’s robust performance, achieving a throughput of 119.683 kbps for efficiently transmitting complete LoRa frames while maintaining a symbol error rate below 0.1.

Estimating service capabilities for logistics terminal stations is essential for guiding operations adjustments to enhance customer experience. However, existing studies often focus on isolated metrics like on-time delivery or complaint rates, each reflecting a specific aspect of service capabilities. To provide a more comprehensive evaluation, we design AdaService, an Adaptive multi-faceted Service capabilities co-estimation framework. We begin by constructing Multi-faceted Hypergraph to encode stations using multiple performance metrics. We then introduce a Multi-faceted Hypergraph Convolution Network (MHCN) to capture the heterogeneous service capabilities across stations, providing a comprehensive capabilities representation. Finally, we apply an Adaptive Multi-faceted Estimation module that uses multi-task learning to model dynamic interactions among these metrics, enhancing predictive accuracy. Extensive evaluation with real-world data collected from nationwide stations in a leading logistics company in China demonstrates that AdaService significantly outperforms state-of-the-art methods, improving estimation accuracy for on-time delivery, on-time pick-up, and complaint rates by up to 18.98%, 9.30%, and 39.62%.

Jiaming Ji, Kaile Wang, Tianyi Alex Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

In last-mile logistics, couriers are typically assigned fixed zones to perform door-to-door deliveries. In practice, packages in some delivery zones might not be fulfilled on time due to couriers taking irregular leave for sickness or higher-priority task assignments, e.g., services for VIPs and regulatory training. Beyond the costly real-world practice, i.e., hiring temporary workers, analysis of historical data reveals that a daily delivery task rescheduling among on-duty couriers can be a cost-effective and efficient alternative. It involves individual workload assessments and delivery task assignments, both of which existing methods can not address adequately: (i) Existing courier workload assessment methods are not tailored for downstream optimization tasks, leading to poor performance. (ii) Efficiency-oriented task assignment methods may lead to unfair workload among the couriers. To address the above two limitations, in this paper, we propose D3-TR, a data-driven method for task reassignment among present couriers. Firstly, we design a consistency-guided predictor that can quickly and precisely predict the workload of couriers. Secondly, based on this predictor, we design a workload-aware genetic algorithm to solve the optimal task allocation problem. Experimental results underscore the superiority of our method over several baselines. Furthermore, real-world deployment on millions of orders demonstrates the effectiveness of our solution, yielding an average of 3.9% improvement in the on-time delivery rate.

Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resource-constrained scenarios. Though existing ARD approaches enhance student model's robustness, the inevitable by-product leads to the degraded performance on clean examples. We summarize the causes of this problem inherent in existing methods with dual-teacher framework as: 1. The divergent optimization objectives of dual-teacher models, i.e., the clean and robust teachers, impede effective knowledge transfer to the student model, and 2. The iteratively generated adversarial examples during training lead to performance deterioration of the robust teacher model. To address these challenges, we propose a novel Cyclic Iterative ARD (CIARD) method with two key innovations: a. A multi-teacher framework with contrastive push-loss alignment to resolve conflicts in dual-teacher optimization objectives, and b. Continuous adversarial retraining to maintain dynamic teacher robustness against performance degradation from the varying adversarial examples. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CIARD achieves remarkable performance with an average 3.53 improvement in adversarial defense rates across various attack scenarios and a 5.87 increase in clean sample accuracy, establishing a new benchmark for balancing model robustness and generalization. Our code is available at https://github.com/eminentgu/CIARD

Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit modeling paradigm that incorporates factors influencing solid deformation through structured modules. Specifically, we present Unisoma, a unified and flexible Transformer-based model capable of handling variable numbers of solids. Unisoma directly captures physical interactions using contact modules and adaptive interaction allocation mechanism, and learns the deformation through a triplet relationship. Compared to implicit modeling techniques, explicit modeling is more well-suited for multi-solid systems with diverse coupling patterns, as it enables detailed treatment of each solid while preventing information blending and confusion. Experimentally, Unisoma achieves consistent state-of-the-art performance across seven well-established datasets and two complex multi-solid tasks. Code is avaiable at [https://github.com/therontau0054/Unisoma](https://github.com/therontau0054/Unisoma).

With the rapid development of Internet of Things (IoT) technologies, Cross-Technology Communication (CTC) has become a key method for establishing direct links between heterogeneous wireless systems. Existing CTC approaches typically rely on emulating the waveform of the target signal; however, such emulation often faces precision limitations due to bandwidth differences, mismatched modulation schemes, and hardware non-idealities. This paper proposes WiBlue, a CTC scheme that enables direct wireless data transmission from Wi-Fi devices to Bluetooth Low Energy (BLE) devices. The approach is based on the IEEE 802.11n physical layer and uses cyclic prefix (CP) processing, quadrature amplitude modulation (QAM), and frequency compensation mechanisms to achieve efficient compatibility across heterogeneous physical layers. WiBlue has been implemented on the USRP B210 platform with commercial BLE devices. Experimental results show that WiBlue achieves high reliability in various indoor environments, maintaining a packet reception rate above 96 % and a symbol error rate below 0.2, demonstrating its practicality and stability for heterogeneous wireless communication.

The growing heterogeneity of wireless ecosystems calls for a unified framework that enables seamless interaction among diverse communication protocols. This paper introduces NNUT (Neural Network-based Wi-Fi Universal Transmitter), a novel approach that leverages deep learning to achieve cross-protocol data transmission without any hardware modification or firmware rewriting. Unlike conventional Cross-Technology Communication (CTC) methods that depend on customized transceivers or handcrafted signal emulation, NNUT employs interpretable neural network modules to emulate key physical-layer operations-FFT, IFFT, and QAM–through lightweight and data-driven architectures. By learning the waveform transformation behaviors of Wi-Fi, ZigBee, LoRa, and Bluetooth, NNUT dynamically generates compatible signals that facilitate direct cross-technology communication. The design integrates 1D transposed convolutional and linear layers to approximate frequency-domain transformations, combined with differentiable subcarrier selection and pilot insertion mechanisms to ensure robust performance under varying channel conditions.

Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are designed for general instances without considering specific problem properties and hard to accurately handle with complex elastic-plastic solids involving contact, loading and unloading. In this work, we take stretch bending, a popular metal fabrication technique, as our case study and introduce LaDEEP, a deep learning-based surrogate model for La rge De formation of Elastic-Plastic Solids. We encode the partitioned regions of the involved slender solids into a token sequence to maintain their essential order property. To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input. Empirically, LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47% relative improvement on average compared to other deep learning baselines. We have also deployed our model into a real-world industrial production system, and it has shown remarkable performance in both accuracy and efficiency. Code is available at https://github.com/therontau0054/LaDEEP.

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43%, underscoring its exceptional efficacy in real-world applications.

2D images and 3D point clouds are foundational data types for multimedia applications, including real-time video analysis, augmented reality (AR), and 3D scene understanding. Class-incremental semantic segmentation (CSS) requires incrementally learning new semantic categories while retaining prior knowledge. Existing methods typically rely on computationally expensive training based on stochastic gradient descent, employing complex regularization or exemplar replay. However, stochastic gradient descent-based approaches inevitably update the model's weights for past knowledge, leading to catastrophic forgetting, a problem exacerbated by pixel/point-level granularity. To address these challenges, we propose CFSSeg, a novel exemplar-free approach that leverages a closed-form solution, offering a practical and theoretically grounded solution for continual semantic segmentation tasks. This eliminates the need for iterative gradient-based optimization and storage of past data, requiring only a single pass through new samples per step. It not only enhances computational efficiency but also provides a practical solution for dynamic, privacy-sensitive multimedia environments. Extensive experiments on 2D and 3D benchmark datasets such as Pascal VOC2012, S3DIS, and ScanNet demonstrate CFSSeg's superior performance.

Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for different domains. To tackle the CDSR, many methods are focused on domain overlapped users’ behaviors fitting, which heavily relies on the same user’s different-domain item sequences collaborating signals to capture the synergy of cross-domain item-item correlation. Indeed, these overlapped users occupy a small fraction of the entire user set only, which introduces a strong assumption that the small group of domain overlapped users is enough to represent all domain user behavior characteristics. However, intuitively, such a suggestion is biased, and the insufficient learning paradigm in non-overlapped users will inevitably limit model performance. Further, it is not trivial to model non-overlapped user behaviors in CDSR because there are no other domain behaviors to collaborate with, which causes the observed single-domain users’ behavior sequences to be hard to contribute to cross-domain knowledge mining. Considering such a phenomenon, we raise a challenging and unexplored question: How to unleash the potential of non-overlapped users’ behaviors to empower CDSR? To this end, we propose a novel CDSR framework with Neural Processes (NP), briefly termed CDSRNP, where NP combines the advantages of meta-learning and stochastic processes. As a meta-learning based method, we first sample some observed overlapped users’ behaviors as the support set to empower query users’ prediction. Next, we employ the NP principle to align the cross-domain correlation prior/posterior distributions generated by support/query user sets, thus the query user (e.g., non-overlapped user) behaviors sequence could also establish a straight bridge to connect other domain items. Additionally, we design a fine-grained interest adaptive layer to identify the users’ interests to enhance prediction. Experimental results illustrate that CDSRNP1 outperforms state-of-the-art methods in two real-world datasets.

Cross-Technology Communication (CTC) is a cornerstone for seamless interoperability in heterogeneous wireless environments, enabling diverse devices to coexist and cooperate effectively. In this paper, we present Wi-Fi-to-X, designed to leverage deep learning techniques to generate waveforms that are compatible with multiple communication protocols, allowing seamless data transmission between Wi-Fi and other wireless technologies such as ZigBee, LoRa, and Bluetooth. This approach enables devices operating under different wireless standards to communicate effectively without requiring hardware modifications or protocol standardization. By training a specialized neural network on simulations of Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM), we have improved the efficiency and reliability of signal processing in CTC, Wi-Fi-to-X achieves robust signal modulation and demodulation across disparate technologies, enabling communication from Wi-Fi to other IoT devices, including ZigBee, LoRa, and Bluetooth. We evaluated both USRP and commodity devices, demonstrated that Wi-Fi-to-X can achieve concurrent wireless communication from Wi-Fi to other IoT devices.

An efficient last-mile delivery scheme in logistics benefits customers, couriers, and the platform. In practice, the delivery scope of a delivery station is divided into multiple areas, each of which is covered by a courier. The long distances between the delivery station and areas limit the couriers' delivery efficiency given that they need to travel back and forth multiple times a day. To solve this problem, we explore an inter-trip cooperation scheme for last-mile delivery, in which couriers traveling to the delivery station and back to corresponding areas earlier can help to take others' orders back. Coordinating the courier cooperation is challenging because we need to consider the courier's status, e.g., locations, and vehicle capacity constraint simultaneously. In this work, we design an inter-trip cooperation-based last-mile delivery system, InCo, aiming to minimize the average order delivery time. InCo includes two components: i) a time-aware spanning tree algorithm to generate the cooperation result for a group of couriers; and ii) a capacity-constrained courier grouping algorithm to optimize the courier grouping result iteratively. Extensive evaluation results with real-world order data collected from one of the largest logistics companies show that InCo improves the average saved delivery time and reduces average travel time by up to 80.2% and 28.4%, respectively, compared to baseline methods. The deployment results show InCo improves the average courier working efficiency by 21.6% to the state-of-the-practice.

Abstract Navigation is a fundamental component of modern information application systems, ranging from military, transportations, and logistic, to explorations. Traditional navigations are based on an absolute coordination system that provides a precise map of the physical world, the locations of the moving objects, and the optimized navigation routes. In recent years, many new emerging applications have presented new demands for navigation, e.g., underwater/underground navigations where no GPS or other localizations are available, an un-explored area with no maps, and task-oriented navigations without specific routes. The advances in IoT and AI enable us to design new navigation paradigms, embodied navigation that allows the moving object to interact with the physical world to obtain the local map, localize the objects, and optimize the navigation routes accordingly. We make a systematic and comprehensive review of research in embodied navigation, encompassing key aspects on perceptions, navigation and efficiency optimization. Beyond advancements in these areas, we also examine the emerging tasks enabled by embodied navigation which require flexible mobility in diverse and evolving environments. Moreover, we identify the challenges associated with deploying embodied navigation systems in the real world and extend them to substantial areas. We aim for this article to provide valuable insights into this rapidly developing field, fostering future research to close existing gaps and advance the development of general-purpose autonomous systems grounded in embodied navigation.

In multi-platform mobile crowdsensing (MCS), platforms attract mobile workers to participate in sensing tasks and collect data through incentive mechanisms to provide data-driven services. However, existing studies often focus exclusively on either competition or cooperation mechanisms between platforms, overlooking scenarios where both coexist. Additionally, most studies prioritize optimizing social welfare or market fairness, neglecting the core role of platforms as data service providers in a free market and the impact of their dominant market position, which limits the utility of platform benefit optimization. In addition, the issue of privacy protection has not received sufficient attention. To this end, this paper proposes a two-stage hybrid mechanism (TS-HM) with the goal of maximizing the utility of the platform, including a decentralized multi-agent reinforcement learning pricing mechanism (DC-PM) and a two-substage cooperative matching mechanism (CMM). In the first stage, the DC-PM mechanism is used to help the platform learn the optimal pricing strategy under privacy-preserving conditions by modeling the pricing and worker contribution problem as a multi-leader-multi-follower stackelberg game; and in the second stage, the CMM mechanism is used to guarantee the matching stability and further enhance the platform utility. Experimental simulations demonstrate that the DC-PM mechanism effectively achieves rapid convergence of pricing strategies while ensuring privacy protection, and the CMM mechanism excels in matching stability and platform performance improvement. In general, the TS-HM mechanism outperforms existing approaches by increasing the total utility of the platform by an average of approximately 12. 65%, significantly increasing the effectiveness of the platform and showcasing its strong advantages.

In the rapidly evolving landscape of cloud-edge computing, efficient resource scheduling across Kubernetes clusters is essential for optimizing microservice deployment. Traditional scheduling methods,e.g.,heuristic and meta-heuristic algorithms, often struggle with the dynamic and heterogeneous nature of cloud-edge environments, relying on fixed parameters and lacking adaptability. We propose and implement DRKC, a novel deep reinforcement learning-based approach that addresses these challenges by improving resource utilization and balancing workloads. We model the scheduling problem as a Markov decision process, enabling DRKC to automatically learn optimal scheduling policies from real-time system data without relying on predefined heuristics. The work synthesizes state information from multiple clusters, using multidimensional resource awareness to effectively respond to changing conditions. We evaluate our performance in three Kubernetes clusters with thirteen nodes and ninety-six test applications with different resource requirements. Experimental results validate the effectiveness of DRKC in enhancing overall resource efficiency and achieving superior load balancing across cloud-edge environments.

Individual cellular networks have been very robust to random cell tower failures due to redundant cell tower deployments. However, a large-scale clustered failure with multiple cell towers can lead to the loss of services of a cellular network. Recently, off-the-shelf smartphones can support multiple network standards, so cellular network infrastructure sharing is a promising direction to improve the service robustness under potential large-scale clustered tower failures. The existing work on cellular network robustness is usually limited to large-scale studies of individual networks or small-scale studies of multiple networks. In this work, we conduct the first investigation, to our knowledge, on cross-network infrastructure sharing benefits for enhancing robustness with afull cellular penetration rate. Our work is based on all cellular networks in Shenzhen, China, covering over 10 million cellular users. Specifically, we design a new metric to quantify cellular network robustness with or without cross-network sharing under both random and clustered cell tower failures. We further study the impact of different factors on robustness, including the number of networks, spatiotemporal dynamics, contextual factors, and a case study at two key transportation hubs. We provide a set of lessons learned based on our study, along with discussions of the results.

Wi-Fi is the de facto standard for providing wireless access to the Internet in the 2.4 GHz ISM band. Tens of billions of Wi-Fi devices (e.g., smartphones) have been shipped worldwide with limited types of wireless radios operating only when Wi-Fi connectivity is available, making it challenging to access data in heterogeneous IoT devices. However, the direct connection between Wireless Personal Area Network (WPAN) technologies, such as Bluetooth, and Wi-Fi presents challenges due to the inherent distinct physical layer. In our work, a novel communication method called BlueWi has been introduced, which serves as a cross technology communication method that enables BLE devices to establish connections and engage in communication with Wi-Fi based WPAN networks. We let BLE signals hitchhike on ongoing Wi-Fi signals, enabling Wi-Fi to recognize specific BLE signal waveforms in the frequency domain. By analyzing the decoded Wi-Fi payload, BlueWi can retrieve the BLE data, ensuring this method remains fully compatible with existing commodity Wi-Fi hardware. The direct sequence spread spectrum scheme is appended to handle general BLE frames and can be considered as “COPY” operation, which allows for better correlation and detection of the signal at the receiver. Evaluations conducted using both USRP and commodity devices have demonstrated that BlueWi can achieve concurrent wireless communication from BLE commercial chips to Wi-Fi networks with a frame reception rate exceeding 96%.

An address, a textual description of a geographical location, plays an important role in location-based services such as instant delivery. However, abnormal addresses (i.e., an address without detailed or accurate information) have led to significant costs. In real-world settings, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries from mobile devices. In this study, we designFastAddr, a fast abnormal address detection framework, which detects abnormal addresses in real time.FastAddrconsists of a novel contrastive address augmentation module and a lightweight multi-head attention model. We further designFastAddr+ to enhanceFastAddrby utilizing large-scale spatial entities. A comprehensive three-phase evaluation is conducted. (i) We evaluateFastAddron a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with a similar detection time. (ii) An offline A/B test shows thatFastAddroutperforms the previous model significantly. (iii) We also conduct an online A/B test to compareFastAddrwith the deployed model, which shows an improvement of F1 by more than 20%. Moreover, we conduct two case studies on real industry data, demonstrating both the efficiency and effectiveness ofFastAddr.

Efficient order assignment in last-mile delivery benefits customers, couriers, and the platform. State-of-the-practice order assignment is based on the static delivery area partition, which cannot adapt well to the dynamic order quantity and destination distributions on different days. State-of-the-art methods focus on balancing order amounts or the payoff among couriers dynamically, neglecting the courier's workload in delivering orders. This paper explores the courier's heterogeneous behaviors for delivering orders to different destinations to measure the courier's workload and then achieve more efficient order assignments under the fair workload constraint. We design a workload-constrained order assignment system, calledWORD, to reduce the cost of the last-mile delivery, i.e., the couriers’ total travel distance and overdue order rate. Specifically, the heterogeneous behaviors for delivering orders are first utilized for workload calculation. Then a two-stage order assignment framework is designed, including a sort-based initialization algorithm for initializing the assignment under the fair workload constraint and a coalition-game-based improvement algorithm for improving the assignment. Extensive evaluation results with real-world logistics data from one of the largest logistics companies in China show thatWORDreduces the cost of the order assignment by up to 51.9% under the fair workload constraint compared to the state-of-the-art methods.

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band.

Recently, integrated warehouse and distribution logistics systems are widely used in E-commerce industries to adjust to constantly changing customer demands. It makes the prediction of purchase demand and delivery supply capacity a crucial problem to streamline operations and improve efficiency. The interaction between such demand and supply not only relies on their economic relationships but also on consumer psychology caused by daily events, such as epidemics, promotions, and festivals. Although existing studies have made great efforts in the joint prediction of demand and supply considering modeling the demand-supply interactions, they seldom refer to the impacts of diverse events. In this work, we propose MulSTE, a Multi-view Spatio-Temporal learning framework with heterogeneous Event fusion. Firstly, an Event Fusion Representation (EFR) module is designed to fuse the textual, numerical, and categorical heterogeneous information for emergent and periodic events. Secondly, a Multi-graph Adaptive Convolution Recurrent Network (MGACRN) is developed as the spatio-temporal encoder (ST-Encoder) to capture the evolutional features of demand, supply, and events. Thirdly, the Event Gated Demand-Supply Interaction Attention (EGIA) module is designed to model the demand-supply interactions during events. The evaluations are conducted on two real-world datasets collected from JD Logistics and public websites. The experimental results show that our method outperforms state-of-the-art baselines in various metrics.

Mobile crowd sensing (MCS) has attracted extensive attention as a promising method for environmental sensing and data collection. However, due to the increasing computational tasks, bandwidth, and computing pressure, traditional “Cloud-Edge–End” MCS is insufficient to efficiently offload sensing data in real-time for data aggregation. Therefore, we consider a novel MCS framework based on the “Cloud-Enhanced-Edge–End,” the framework uses MCS idle users as edge nodes (ENs) to assist edge servers to enhance computing power and reduce delay and energy consumption. To achieve efficient data aggregation in the “Cloud-Enhanced-Edge–End” MCS framework, we first consider the multiobjective optimization of delay and energy consumption to establish a utility function. Second, addressing the shortcomings of traditional optimization algorithms, which often struggle with complex decision spaces and dynamic environments, we propose a MCS offloading (MCSOL) algorithm based on deep Q-network (DQN). The algorithm uses reinforcement learning to adaptively offload computing tasks to the optimal ENs or servers to maximize utility function to reduce delay and energy consumption. Our experimental results reveal that, in comparison with the other five strategies like only offloading data to base stations and ENs, MCSOL enhances data aggregation performance within the range of 30%–60%.

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services such as Google Maps. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses. To achieve this, we take the E-Commerce logistics service as a concrete scenario and designCoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data.CoMinerincludes three main components, (1) A POI-level clustering model, (2) A Delivery Mobility Graph (DMG), and (3) A behavior-driven address ranking model. Furthermore, we designCoMiner-W, a coordinates mining algorithm based on WiFi data, to further enhance the effectiveness ofCoMiner. We conduct extensive experiments on three large-scale datasets whereCoMineroutperforms the state-of-the-art methods by 20.3%. Moreover, we have designed an abnormal delivery event detection system based onCoMinerand deployed it at JD Logistics, which brings a significant reduction in abnormal delivery event rates.

Points of Interest (POIs), such as entertainment, dining, and living, are crucial for urban planning and location-based services. However, the high dynamics and expensive updating costs of POIs pose a key roadblock for their urban applications. This is especially true for developing countries, where active economic activities lead to frequent POI updates (e.g., merchants closing down and new ones opening). Therefore, POI updating, i.e., detecting new POIs and different names of the same POIs (alias) to update the POI database, has become an urgent but challenging problem to address. In this paper, we attempt to answer the research question of how to detect and update large-scale POIs via a low-cost approach. To do so, we propose a novel framework called UrbanPOI, which formulates the POI updating problem as a tagging and detection problem based on multi-modal logistics delivery data. UrbanPOI consists of two key modules: (i) a hierarchical POI candidate generation module based on the POINet model that detects POIs from shipping addresses; and (ii) a new POI detection module based on the Siamese Attention Network that models multi-modal data and crowd intelligence. We evaluate our framework on real-world logistics delivery datasets from two Chinese cities. Extensive results show that our model outperforms state-of-the-art models in Beijing City by 26.2% in precision and 10.7% in F1-score, respectively.

Last-mile delivery from delivery stations to customers’ places is now mainly finished by dedicated couriers. In practice, each courier generally collects orders destined for one delivery area at the delivery station and delivers orders to customers. However, the long distance between the delivery station and the delivery area due to practical reasons, e.g., expensive delivery station rental fee in the downtown area, increases the delivery courier’s travel time and decreases the efficiency of the state-of-the-practice last-mile delivery scheme. In this paper, we solve the problem with relay transportation, where a relay courier collects orders at the delivery station and sends them to delivery couriers, and delivery couriers focus on the order delivery at corresponding delivery areas. We design a real-time relay courier scheduling system called REDE to minimize the average relay order delivery time (ARODT) considering the relay and delivery couriers’ mobility and the order destination distribution. First, a heterogeneous task aware route prediction algorithm is proposed to characterize the delivery courier’s mobility. Then a distance-aware greedy algorithm and an ARODT-constrained exchange algorithm are designed to generate the relay route, which is updated with real-time order pickup requests. Extensive evaluation results with real-world logistics data from 100 delivery stations in 38 cities show that REDE reduces ARODT by up to 8.4% compared to baseline methods. The online A/B tests show that compared to the state-of-the-practice method, REDE improves the delivery courier’s working efficiency and the daily number of pickup orders by 20.13% and 4.51%, respectively.

Order Transfer from the transfer center to delivery stations is an essential and expensive part of the logistics service chain. In practice, one vehicle sends transferred orders to multiple delivery stations in one transfer trip to achieve a better trade-off between the transfer cost and time. A key problem is generating the vehicle’s route for efficient order transfer, i.e., minimizing the order transfer time. In this paper, we explore fine-grained delivery station features, i.e., downstream couriers’ remaining working times in last-mile delivery trips and the transferred order distribution to design a Prediction-and-Scheduling framework for efficient Order Transfer called PSOT, including two components: i) a Courier’s Remaining Working Time Prediction component to predict each courier’s working time for conducting heterogeneous tasks, i.e., order pickups and deliveries, with a context-aware location embedding and an attention-based neural network; ii) a Vehicle Scheduling component to generate the vehicle’s route to served delivery stations with an order-transfer-time-aware heuristic algorithm. The evaluation results with real-world data from one of the largest logistics companies in China show PSOT improves the courier’s remaining working time prediction by up to 35.6% and reduces the average order transfer time by up to 51.3% compared to the state-of-the-art methods.

The popularity of online e-commerce has promoted the rapid development of last-mile logistics in recent years. In last-mile services, to ensure delivery efficiency and enhance user experience, the delivery zone is proposed to perform delivery task assignment, which is a fundamental part of last-mile delivery. Each courier is responsible for one delivery zone. Couriers will collect orders belonging to their delivery zones from the delivery station and deliver orders to customers. Existing delivery zone partition practices in last-mile logistics consist of manual experience-based and static optimization-based methods, which perform order amount balancing among different zone but suffer from dissatisfaction and inefficiency because of two limitations: (i) using order amount is not always a good balancing metric considering deliveries' various difficulties (e.g., residence or industrial park, with or without elevators); (ii) less considering couriers' familiarity and preference behaviors. To generate delivery zone partition with equitable workload assignment, in this paper, we propose E-partition, a data-driven delivery zone partition framework to achieve equitable workload assignment in last-mile logistics. We first design a learning-based workload prediction model to estimate service time given a partition plan that consists of unseen courier-zone matching scenarios. Then, a delivery zone partition algorithm is proposed to iterative optimize couriers' core-AOI (i.e., area of interest) generation and AOI assignment process. Extensive offline experimental results show that our model outperforms baselines in working time prediction and workload balancing performances. Real-world deployment results at JD Logistics also verify the effectiveness of equitable-assignment aware delivery zone partition, with a 2.2% increase in service on-time rate compared to state-of-practice partition solutions.

In mobile crowd sensing (MCS), most of the research work does not consider the dropout situation of the workers, resulting in a lower completion rate of the tasks. To decrease the performance loss caused by the dropout of workers, one solution is to prevent workers from dropping out in advance. However, this solution cannot be widely applied to some emergent scenarios and avoid dropouts strictly. In this paper, we consider another solution to respond to worker dropouts actively and innovatively propose a two-stage incentive framework, namely the Game and Reverse Auction based Incentive Mechanism (GRAIM), which aims at motivating dropout workers to submit perceived data and effectively recruiting high-quality idle workers to complete all unfinished tasks, including the tasks left behind by the dropout workers and other incomplete tasks. In the first stage, a bargaining game-based reward allocation method (BGRA) is proposed to incentivize the perceived data submission of dropout workers with a reward equilibrium between the platform and the dropout workers. In the second stage, worker recruitment is modeled as a multi-armed bandits (MAB) issue in a reverse auction, and the extended upper confidence bound (EUCB) algorithm is proposed for the platform to recruit high-quality idle workers to perform unfinished tasks. The experimental results show that compared with the state-of-art mechanisms, our proposed GRAIM increases social welfare by about 15%~ 23%, reduces average reward by about 16% ~ 25%, and increases task completion rates by about 8% ~ 20%.

Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee, and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in cross-technology communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.

Federated learning (FL) is a machine learning technique that allows for on-site data collection and processing without sacrificing data privacy and transmission. Heterogeneity is a key challenge in federated settings. Recently, cross-technology communication (CTC) has emerged as a solution for Internet of Things (IoT) heterogeneity, enabling direct communication between different wireless devices without the need for hardware modifications or gateway intervention. For example, a sophisticated WiFi device can serve as a central coordinator for other heterogeneous devices, such as LoRa, ZigBee, Bluetooth, and LTE, leading to more efficient and ubiquitous cross-network information exchange. However, heterogeneous wireless technologies present different data transmission rates and computing resources, making it difficult to achieve high accuracy in predictions due to large amounts of multidimensional data, communication delays, transmission latency, limited processing capacity, and data privacy concerns. In this work, we propose an FL framework based on CTC for heterogeneous IoT applications, called FLCTC. To demonstrate the usability of FLCTC, we implemented FLCTC and a specific solution for forest fire prediction. FLCTC was concretely implemented as a federal deep learning based on long and short-term memory and used for forest fire prediction, addressing the challenge of data characterization in heterogeneous IoT networks. FLCTC promises to improve communication efficiency and prediction accuracy. Our platform-based evaluation results show that FLCTC is feasible, with a recall of 96% and an accuracy of 88%, offering valuable insights into the use of FL with CTC for heterogeneous IoT applications.

With the rapid development of sharing economy, we have access to massive sharing systems such as Uber, Airbnb, and bike sharing nowadays. The sharing economy, at its core, is to achieve efficient use of resources. However, the actual usage of shared resources is still unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage patterns with the large-scale data collected from these sharing systems. In this paper, we first analyze the shared bike usage patterns in three typical bike sharing systems based on 140-month multi-event data. From our data-driven analysis, we found that the most used 20% of shared bikes account for 45% of total usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare+ to improve the bike sharing systems by considering both the quality of service and bike maintenance, which includes three key components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy and contextual LSTM-based prediction model with both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping-based evaluation methodology to measure the efficiency of eShare+ with data from three large-scale bike sharing systems including 84,000 bikes and 3,300 service stations. Our results show that eShare+ not only fully utilizes shared bikes with efficient maintenance but also improves the quality of service. In addition, eShare+ also has the potential to be applicable to different fleet sizes.

Several recent works have studied mobile charging under the “one-to-many” charging pattern where a single charger can charge multiple devices simultaneously. However, most of them focus on path planning and charging time allocation, but overlook the underlying dependence of the charging efficiency on initial deployment positions of chargers. This paper studies the problem ofPlacing directional wIreless chargers withLimited mObiliTy (PILOT), that is, maximize the overall charging utility for a set of static rechargeable devices on a 2D plane by determining deployment positions, stop positions and orientations, and portions of time for all deployed chargers that can move in a limited area after their deployment. To the best of our knowledge, we are the first to study placement of mobile directional chargers under the “one-to-many” pattern. To address PILOT, we propose a$(\frac{1}{2}-\epsilon)$-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms six comparison algorithms by$19.74 \% \sim 500.01 \%$.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy calledVALIDin 364 Chinese cities.VALIDis pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12-2021/01),VALIDconsists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers’ behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants’ smartphones under their consent as a virtual infrastructure to design, build, deploy, and operateVALIDfrom in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers’ behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens’ daily lives significantly. In the fulfillment cycle, the order preparation time estimation is extremely important and can be used for many applications, such as improving order dispatching and fulfillment time estimation. Existing work is generally based on high-cost physical devices or large-scale labeled training data, which are not feasible in on-demand delivery services. We solve this problem based on already collected different kinds of data from the on-demand delivery platform, e.g., the courier’s reported arrival time to the merchant. Our intuition is that the couriers’ reported time implicitly reflects the order preparation time, which leads to a challenge: complicated correlations between the couriers’ reported arrival time and the order preparation time. To solve this challenge, we design an order preparation time inference framework OPTI, which first constructs a self-supervised classification task based on the couriers’ reported arrival time to infer the coarse-grained order preparation time and then exploits semi-supervised learning to transfer the coarse-grained time to fine-grained time inference. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.

The popularity of e-commerce has promoted the rapid development of the logistics industry in recent years. As an important step in logistics, last-mile delivery from delivery stations to customers' addresses is now mainly finished by couriers, which requires accurate workload assessment based on actual efforts. However, the state-of-the-practice assessment methods neglect a vital factor that orders with the same customer's address (i.e., Homogeneous orders) can be delivered in a group (i.e., in a single trip) or separately (i.e., in multiple trips). It would cause unfair assessment among couriers if following the same rule. Thus, grouping homogeneous order accurately in the workload assessment is significant for achieving fair courier's workload assessment. To this end, we design, implement, and deploy a nationwide homogeneous order grouping system called FHOG for improving the accuracy of homogeneous order grouping in last-mile delivery for fair courier's workload assessment. FHOG utilizes the courier's reporting behavior for order inspection, collection, and delivery to identify homogeneous orders in the delivery station simultaneously for homogeneous order grouping. Compared with the state-of-the-practice method, our evaluation shows FHOG can effectively reduce order amounts with the higher and lower assessed courier's workload. We further deploy FHOG online in 8336 delivery stations to provide homogeneous order grouping service for more than 120 thousand couriers and 12 million daily orders. The results of the two surveys show that the couriers' acceptance rate is improved by 67% with FHOG after the promotion.

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services (e.g., Google Maps) by uploading textual addresses to obtain corresponding coordinates. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses for service providers. To achieve this, we take the E-Commerce logistics service as a concrete scenario and design CoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data. There are three main components in CoMiner. (1) A POI-level clustering model by modeling customers' shopping patterns at different spatial granularities; (2) A Delivery Mobility Graph (DMG) by modeling couriers' delivery events and geographic coordinates; (3) A behavior-driven address ranking model by mining couriers' uncertain reporting behaviors to further infer coordinates on DMG. We extensively verify the performance of CoMiner with a three-phase evaluation from data-driven experiments to real-world deployment. (i) We conduct extensive experiments on three large-scale datasets where CoMiner achieves an average accuracy of 95.1%, which outperforms the state-of-the-art methods by 20.3%. (ii) We deploy CoMiner in JD Logistics, inferring coordinates for over 30 million addresses with an average accuracy of 93.3%. (iii) We utilize CoMiner for two Geocoding-based applications, i.e., parcel re-routing optimization and abnormal delivery event detection.

An address, a textual description of a physical location, plays an important role in location-based services such as on-demand delivery and e-commerce. However, abnormal addresses (i.e., an address without detailed information representing a spatial location) have led to significant costs. In real-world settings like e-commerce, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries. In this study, we design FastAddr, a fast abnormal address detection framework, which detects abnormal addresses among millions of addresses in a short time. By investigating and modeling the hierarchical structure of address data, we first design a novel contrastive address augmentation approach to generate training data via learning the entity transition probability matrix. We further design a lightweight multi-head attention model for learning compact address representation by modeling the address characteristics. We conduct a comprehensive three-phase evaluation. (i) We evaluate FastAddr on a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with similar detection time. (ii) An offline A/B test shows that FastAddr outperforms the previous deployed model significantly. (iii) We also conduct an online A/B test to compare FastAddr with the deployed model, which shows an improvement of F1 by more than 20%. Moreover, a real-world case study demonstrates both the efficiency and effectiveness of FastAddr.

We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) calledaBeacon(short foralibabaBeacon).aBeaconis pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04),aBeaconconsists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of$\$ $600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operateaBeaconfrom its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing.

Today’s mobile video codecs suffer from vulnerability to the high network dynamics. When the network effective bandwidth varies in a large range and is not predictable, e.g., in a high-speed mobile environment or being with intense wireless interference, the videos are continuously choppy and jerky, waiting for the buffer to fill before playing. In this paper, we design and implement a novel video codec scheme called DVC that provides the following properties: 1) Keep the video compression rate as the modern video codec schemes, such as H.265; 2) Non-blocking playback for video decoding; 3) High tolerance to network dynamics. Different from existing robust video codec designs that try to provide certain redundancy to compensate the network transmission losses, DVC assumes that transmission losses are inevitable. And thus the design philosophy is to mitigate the consequence of the data loss by restraining the error propagation caused by the losses, rather than the loss itself. We implement DVC with a encoder and a decoder and conducted comprehensive evaluations based on the prototype systems. Experimental results show that the video quality (PSNR) can be significantly improved by 4dB to 10dB, espeically when the videos have low amount of movement, e.g., news report.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12- 2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers' behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants' smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers' behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

Hierarchical Modulation (HM) is quite efficient for multimedia broadcast in wireless networks. It exploits the wireless broadcast advantage and allows a transmission to reach different users with various qualities. However, existing HM related schemes are based on specially designed hardware, leading to extra hardware and deployment cost in practice. As a remedy for this, we design and implement a software-based HM scheme, named as SoftHM. By carefully tuning the Physical Layer input of a packet (a sequence of “0” or “1” bits, known as PHY payload), a transmitter simply broadcasts the packet with a single rate (e.g., 54Mbps, the highest rate in 802.11g WLAN), while diverse receivers can decode and extract different amount of information (with different reception rate, e.g., 12Mbps or 54Mbps) from it based on their link qualities. Reverse engineering techniques are adopted so that SoftHM works in a plug-in mode, offering it high potentials for wireless systems without any changes in existing modulation and coding modules. We implement SoftHM based on Universal Software Radio Peripheral (USRP) B210 and Commercial of the Shelf (COTS) Wi-Fi devices. Both rigorous analysis on its performance and comprehensive implementation-based experiments show that compared with the traditional time-sharing approach, to support low-quality users with the same bandwidth, high-quality users in SoftHM achieve 111.1% to 616.7% higher throughput. Likewise, to serve high-quality users with the same bandwidth, low quality users in softHM achieve 176.9% to 390.9% higher throughput.

Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we propose a near optimal scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than $(1-\epsilon)$ of the optimal solution to PESA with a smaller EMR threshold $(1-\epsilon /2)R_{t}$ and a larger EMR coverage radius $(1+\epsilon /2)D$ . We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the comparison algorithms.

Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories by sensing natures, i.e., explicit and implicit sensing. In this article, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of two sensing approaches by using two particular systems for traffic sensing as concrete examples. In our investigation, we utilize TB-level data from two systems: (i) GPS data from five thousand vehicles, (ii) signaling data from three million cellphone users, from the Chinese city Hefei. Our study adopts a widely used concept called crowdedness level to rigorously explore the impacts of contexts on traffic conditions including population density, region functions, road categories, rush hours, holidays, weather, and so on, based on various context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios and then we explore the possibility of unifying two sensing approaches for better performance by using a truth discovery-based data fusion scheme. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.

This paper studies the problem of Placing directional wIreless chargers with Limited mObiliTy (PILOT), that is, given a budget of mobile directional wireless chargers and a set of static rechargeable devices on a 2D plane, determine deployment positions, stop positions and orientations, and portions of time for all chargers such that overall charging utility of all devices can be maximized. To the best of our knowledge, we are the first to study placement of mobile chargers. To address PILOT, we propose a (1/2 - ε)-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms five comparison algorithms by 31.33% ~ 281.10%.

Instant delivery is an important urban service in recent years because of the increasing demand. An important issue for delivery platforms is to keep updating the status of couriers especially the real-time locations, which is challenging when they are in an indoor environment. We argue the previous indoor localization techniques cannot be applied in the instant delivery scenario because they require extra deployed infrastructures and extensive labor work. In this work, we perform the couriers' indoor localization transparently in a predictive manner without extra actions of couriers by existing data from the platform including order progress reports and couriers' trajectories. Specifically, we present TransLoc to predict couriers' indoor locations by addressing two challenges including uncertain reporting behaviors and uncertain indoor mobility behaviors. Our key idea lies in two insights (i) couriers' behaviors are consistent in indoor/outdoor environments; (ii) localization, as a spatial inference problem, could be converted to a temporal inference problem. We evaluate TransLoc on 565 couriers from an instant delivery company, which improves baselines by at most 72%, and achieves a competitive result compared to a label-extensive approach. As a case study, we apply TransLoc to optimize the order dispatching strategy, which reduces the delivery time by 24%.

Crowdsourcing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human. Especially, nowadays, no countries, no communities, and no person are immune to urban emergency events. Detection about urban emergency events, e.g., fires, storms, traffic jams is of great importance to protect the security of humans. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media usually includes references to urban emergency events occurring at, or affecting specific locations. In this paper, in order to detect and describe the real time urban emergency event, the 5W (What, Where, When, Who, and Why) model is proposed. Firstly, users of social media are set as the target of crowd sourcing. Secondly, the spatial and temporal information from the social media are extracted to detect the real time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive case studies based on real urban emergency events. The results show the accuracy and efficiency of the proposed method.

In this paper, we deal with the problem of Robust schedUling for wireLess charger nEtworks (RULE), i.e., given a number of rechargeable devices, each of which may drift within a certain range, and a number of directional chargers with fixed positions and adjustable orientations distributed on a 2D plane, determining the orientations of the wireless chargers to maximize the overall expected charging utility while taking the charging power jittering into consideration. To address the problem, we first model the charging power as a random variable, and apply area discretization technique to divide the charging area into several subareas to approximate the charging power as the same random variable in each subarea and bound the approximation error. Then, we discretize the orientations of chargers to deal with the unlimited searching space of orientations with performance bound. Finally, by proving the submodularity of the problem after the above transformations, we propose an algorithm that achieves (1/2-ε)-approximation ratio. We conduct both simulation and field experiments, and the results show that our algorithm can perform better than other comparison algorithms by 103.25% on average.

Most software systems frequently encounter errors when interacting with their environments. When errors occur, error-handling code must execute flawlessly to facilitate system recovery. Implementing correct error handling is repetitive but non-trivial, and developers often inadvertently introduce bugs into error-handling code. Existing tools require correct error specifications to detect error-handling bugs. Manually generating error specifications is error-prone and tedious, while automatically mining error specifications is hard to achieve a satisfying accuracy. In this paper, we propose EH-Miner, a novel and practical tool that can automatically detect error-handling bugs without the need for error specifications. Given a function, EH-Miner mines its error-handling rules when the function is frequently checked by an equivalent condition, and handled by the same action. We applied EH-Miner to 117 applications across 15 software domains. EH-Miner mined error-handling rules with the precision of 91.1% and the recall of 46.9%. We reported 142 bugs to developers, and 106 bugs had been confirmed and fixed at the time of writing. We further applied EH-Miner to Linux kernel, and reported 68 bugs for kernel-4.17, of which 42 had been confirmed or fixed.

With the rapid development of sharing economy, massive sharing systems such as Uber, Airbnb, and bikeshare have percolated into people's daily life. The sharing economy, at its core, is to achieve efficient use of resources. The actual usage of shared resources, however, is unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage status with the large-scale data collected from these sharing systems. In this paper, we analyze the bike usage status in three typical bikeshare systems based on 140-month multi-event data. Our analysis shows that the most used 20% of bikes account for 45% of usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare which has three components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy-based prediction model based on both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping based evaluation methodology and measure the efficiency of eShare with data from three systems including the world's largest bikeshare system with 84,000 bikes and 3,300 stations. Our results show that eShare not only fully utilizes shared bikes but also improves service quality.

We are living in the era of data deluge. Meanwhile, the world of big data includes a rich and complex set of cross-media content, including text, images, video, audio, and graphics. To date, substantial research efforts have been dedicated to big data processing and cross-media mining, yielding good theoretical underpinnings and great practical success. However, studies that jointly consider cross-media big data analytics are relatively sparse (Zhang, Liu, Deng, Xu, and Choo (2018); Zhang, Wei, Wang, and Liao (2018)). This research gap needs more attention since it will benefit many real-world applications. Despite its significance, it is important to analyse cross-media big data due to the heterogeneity (Zhang, Zhao, Li, Chen, and Yuan (2015)), large-scale volume, increasing size, lack of structure, correlations, and noise. Multi-modal information learning (Demertzis, Iliadis, Avramidis, and El-Kassaby (2017)), which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved substantial progress in both academia and industry. Additionally, deep learning has been adopted in all kinds of computer vision applications, and many breakthroughs have been achieved in sub-areas, such as DeepFace in the LFW competition for face verification and GoogleNet for the ImageNet Competition for object categorization. We can expect that more and more computer vision applications will benefit from multi-modal information learning. This special issue focuses on learning methods to achieve high-performance multi-modal information analysis and understanding under uncontrolled environments on a large scale, which is also a very challenging problem. This problem has attracted considerable attention from both academia and industry. We hope this topic will aggregate top-level research on new advances in multi-modal information from cross-media data. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas. After several rounds of review, six papers have been accepted for publication. Zhu, Ma, and Liu (2018) combined status-striving motivation with opinion dynamics to study the decision mechanism and evolution law of employee knowledge sharing behaviour. A multiagent simulation method is used to design the decision-making model of employee knowledge sharing behaviour. The influence of the network structure, number of people, and the proportion of employees with different statuses on knowledge-sharing performance is considered. The simulation software is used to convert the theoretical model into a parametric experiment, and the sensitivity analysis is also employed. According to natural language processing and machine translation technology, to solve the problem, He (2018) aims to establish a parallel corpus for information extraction based on the OntoNotes corpus by combining automatic extraction and manual adjustment. To verify the validity of the parallel corpus constructed in this paper, a comparative experiment was carried out on the corpus. The corpus entity alignment rate, anaphora absence, and syntactic structure were analysed in detail based on statistics. To reduce the vagueness and subjectivity of customer demand in the process of product–service system design, a fuzzy semantic calculation method is proposed by Zhang (2018) to obtain the importance of service demand. In addition, according to the demand of the clustering of service modules, a new clustering method is proposed to analyse discrete data based on the improved K-means algorithm that is based on the Kruskal algorithm. Based on the actual situation of the knowledge flow during industrial transfer, a dynamic model of knowledge flow in a complex network is built by Zhou and Wu (2018). The mechanism of the knowledge flow in the network is analysed, and an analogue simulation is conducted using the Netlogo platform to identify the effects of the networkApostrophe;s average degree, the number of enterprises involved in the transfer, the network size, and the effective transmission rate of the knowledge on the efficiency of the knowledge flow in the network. The work presented by Yang (2018) proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. As an important resource of the company, the customer plays a vital role in the process of knowledge sharing. Therefore, research on the mechanism of customersApostrophe; participation in knowledge sharing is especially important. In the work by Wang and Liu (2019), from the perspective of knowledge sharing between customers and enterprises, a customer participation model of a knowledge-sharing mechanism is built based on three aspects: subjective influencing factors of knowledge sharing, objective influencing factors of knowledge sharing, and environmental influencing factors of knowledge sharing. As the special issue editors, we would like to take this opportunity to thank the various authors for their papers and the reviewers for their work. We are also grateful to Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems.

A public safety event is a danger and urgent event that need early detection, quick response, and accuracy recover. The efficient method for responding to a happening public safety event is to collect and describe the related data. Besides the surveillance cameras from the physical space, the social media data can also be used to collect and describe the related data of a public safety event. In this work, the proposed method focuses on the step for describing public safety events. Given a public safety event, videos from the surveillance cameras and social messages from social sensors are collected. The multi-modal information including texts, images, videos, and spatial-temporal data is mined to give a description precisely and concisely. First, the social sensors are associated to surveillance cameras by the spatial and temporal information. In the second stage, the social messages are associated to surveillance cameras by the semantic information. In the third stage, the social messages are associated to surveillance cameras by the visual feature. Besides the text, social sensors may upload images or videos. Finally, the multi-modal description step is given based on the three different associations. The experiments on the real data demonstrate the superiority of the proposed framework. Case studies on the real public safety event show the proposed model has good performance and high effectiveness.

Efficient spectrum sensing is essential for the successful application of the Dynamic Spectrum Assignment (DSA) technology in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing so that the spectrum opportunities are thoroughly exploited while the primary users are not harmed. In this article, we propose a new sensing service model in which a Spectrum Sensor Network (SSN) is employed for spectrum sensing tasks. We will describe the general framework for this SSN-enabled CRN and present the major challenges in such an architecture. We will address one of these challenges and formulate it as a boundary detection problem with unknown erroneous inputs. A novel cooperative boundary detection algorithm is designed which explores recent advances in Support Vector Machines (SVM) and computational geometry. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. Real testbed as well as comprehensive simulation experiments are conducted, and the results show that, compared with the traditional schemes, cooperative boundary detection can dramatically reduce the spectrum sensing overhead and improve the effectiveness of DSA.

We are witnessing a rapid growth of electrified vehicles because of the ever-increasing concerns over urban air quality and energy security. Compared with other electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to the high owning and operating costs, long charging time, and the uneven distribution of charging facilities. Moreover, the highly dynamic environment factors such as the unpredictable traffic congestions, different passenger demands, and even changing weather, can significantly affect electric bus charging efficiency and potentially hinder further development of large-scale electric bus fleets. To deal with these issues, in this paper, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines and 5,562 bus stops, which is obtained from the Chinese city Shenzhen, who has the first and the largest full electric bus network for public transit. Then we investigate the electric bus network to understand its operating and charging patterns, and further verify the feasibility and necessity of a real-time charging scheduling. With such understanding, we design bCharge, a real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of bCharge, we implement it with the real-world streaming dataset from Shenzhen, which includes GPS data of the electric bus fleet, the bus lines and stops data, coupled with the 376 electric bus charging stations data. The evaluation results show that bCharge can dramatically reduce the charging cost by 23.7% and 12.8% electricity usage simultaneously.

Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories, i.e., explicit and implicit sensing. In this paper, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of these two sensing approaches by using two particular systems for traffic sensing as concrete examples, i.e., a vehicular system as a crowdsourcing-based explicit sensing and a cellular system as an infrastructure-based implicit sensing. In our investigation, we utilize TB-level data from two systems: (i) vehicle GPS data from 3 thousand private cars and 2 thousand commercial vehicles, (ii) cellular signaling data from 3 million cellphone users, from the Chinese city Hefei. Our study adopts a widely-used concept called crowdedness level to rigorously explore the impacts of various spatiotemporal contexts on real-time traffic conditions including population density, region functions, road categories, rush hours, etc. based on a wide range of context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios then we explore the possibility of unifying these two sensing approaches for better performance. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.

Failure-diagnosis logs can dramatically reduce the system recovery time when software systems fail. Log automation tools can assist developers to write high quality log code. In traditional designs of log automation tools, they define log placement rules by extracting syntax features or summarizing code patterns. These approaches are, however, limited since the log placements are far beyond those rules but are according to the intention of software code. To overcome these limitations, we design and implement SmartLog, an intention-aware log automation tool. To describe the intention of log statements, we propose the Intention Description Model (IDM). SmartLog then explores the intention of existing logs and mines log rules from equivalent intentions. We conduct the experiments based on 6 real-world open-source projects. Experimental results show that SmartLog improves the accuracy of log placement by 43% and 16% compared with two state-of-the-art works. For 86 real-world patches aimed to add logs, 57% of them can be covered by SmartLog, while the overhead of all additional logs is less than 1%.

An emergency event is a sudden, urgent, usually unexpected incident or occurrence that requires an immediate reaction or assistance for emergency situations, which plays an increasingly important role in the global economy and in our daily lives. Recently, the web is becoming an important event information provider and repository due to its real-time, open, and dynamic features. In this paper, web resources based states detecting algorithm of an event is developed in order to let the people know of an emergency event clearly and help the social group or government process the emergency events effectively. The relationship between web and emergency events is first introduced, which is the foundation of using web resources to detect the state of emergency events imaged on the web. Second, five temporal features of emergency events are developed to provide the basis for state detection. Moreover, the outbreak power and the fluctuation power are presented to integrate the above temporal features for measuring the different states of an emergency event. Using these two powers, an automatic state detecting algorithm for emergency events is proposed. In addition, heuristic rules for detecting the states of emergency event on the web are discussed. Our evaluations using real-world data sets demonstrate the utility of the proposed algorithm, in terms of performance and effectiveness in the analysis of emergency events.

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold Rt. We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a series of distributed algorithms, including a fully distributed algorithm that provably achieves (1- ϵ) approximation ratio and requires only communications with neighbors within a constant distance for each charger. Through extensive simulation and testbed experiments, we demonstrate that our proposed algorithms can outperform the set-cover algorithm by up to 17.05%, and has an average performance gain of 41.1% over the existing algorithm in terms of the overall charging utility.

Association link network (ALN) can organize massive Web information to provide many intelligent services in our big data society. Effective semantic layered technologies not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of related information systems such as Web information system and industrial information system. How to realize the layer division of association semantic by the hierarchy analysis of ALN is an important research topic. To solve this problem, this paper proposes a hierarchy-cutting model of association semantic. First, experiments of four types of keywords with different linking roles are conducted to discover the possible distribution law. Experimental results show that these keywords with association role reveal previous power-law distribution. Then, based on the discovered power-law distribution, up-cutting and down-cutting points are presented to divide the association semantic into three layers. At the same time, theories of the hierarchy-cutting model are presented. Finally, examples of current core topic and permanent topics belonging to a domain are given. The experiments show that hierarchy-cutting points have high accuracy. The multilayer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle sizes on ALNs.

Among various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is prone to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile during run-time. In a practical dynamic environment, however, the profile frequently changes and the painful retraining is needed. Ratherthan such static ways of profiling the environments, in this paper, we tryto accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove that, this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and modify it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is about 1 meter. Compared with the traditional RSS-based approaches in dynamic environments, the accuracy improves up to 10 times.

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology, which allows the power to be transferred from a charger to ambient devices wirelessly, receives intensive interests. Existing schemes mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the safe charging problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold Rt. We show that SCP is NP-hard and propose a solution, which provably outperforms the optimal solution to SCP with a relaxed EMR threshold (1-ε)Rt. Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when ε = 0.1, while a naive greedy algorithm is 34.6% below our solution.

Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we optimize our scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than (1 - ϵ) of the optimal solution to PESA with a smaller EMR threshold (1 - ϵ/2)Rt and a larger EMR coverage radius (1 + ϵ/2)D. We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the prior art by up to 45.7%.

With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of modern data processing techniques. The past decade has witnessed the tremendous technical advances in sensor networks, Internet/Web of Things, cloud computing, mobile/embedded computing, spatial/temporal data processing, and big data, and these technologies have provided new opportunities and solutions to data processing techniques. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions, and web logs and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision-making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make business more agile, and to answer questions that were previously considered beyond our reach. This special issue wants to demonstrate the emerging issues in the research of big data and approaches towards it. Original and research articles are solicited in all aspects including theoretical studies, practical applications, and experimental prototypes. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. Totally, we have received 20 manuscripts and 11 papers are accepted. Five papers are selected from SKG2015 conference with about 50% new content. This special issue will be published by Concurrency and Computation: Practice and Experience as special issues. In order to detect and describe the real-time urban emergency event, the knowledge base model is proposed by the paper by Z. Xu et al 1. The crowdsourcing-based knowledge base model is introduced, which uses the information from social media. X. Lin et al 2. introduced one such comprehensive schemes: it takes consideration of the DG's own characteristics and its ability to support the local loading, it adopts different protection strategy, and realizes the fault isolation and island division through the coordination between the protection and the automatic devices. J. Zheng 3 extracted the feature information and also mined the association rules to identify and mark the unknown protocol using the learning mechanism of machine. The unknown protocol in a specific environment was found and analyzed by marking the fingerprint information of protocol. M. Xie 4 studied meta-analytically integrated results from piles of network position researches in organizational innovation in 20 years. Considering the real social community network partition approach regardless of the directed and weighted characteristic, K. Gao 5 proposed a novel algorithm in pervasive sensing environment. Start with classifying and comparing current search engines, particularly from the perspective of search patterns which consist of index structure, user profiles, and interaction mechanism. X. Wei 6 then present a novel search pattern named ExNa by defining its model and basic operations in detail. Two attribute reduction methods based on minimum decision cost are proposed by Z. Bi 7 from the algebraic view and the information theory, respectively. Y. Wang 8 designs an optimization algorithm to solve the problem. Numerical experiments show that the optimization algorithm can solve the load imbalance of the METGRID to some extent, and the computation speed of the METGRID and REAL modules after the optimization on 64 cores is about 7.2 times faster than before. Y. Xue 9 proposed Event Space Model for event analysis by multi-viewer. In the model, each event is mapped into Network Public Opinion Data Space (OS) and Actual Behavior Data Space (BS). S. Zhang 10 proposed a model for estimating the out-degree of any one node in ALN from semantic feature view, which can greatly reduce the searching scope for the rapid positioning of Web resources stored in large-scale database. J. Zhang 11 presented how to mine patent text to get valuable information/knowledge from large-scale candidates obtained from these patents based on massive patent texts. The guest editors would like to thank Prof. Geoffrey C. Fox, who is the editor in chief of Concurrency and Computation: Practice and Experience. His help and trust are the most important thing for the success of this special issue. The guest editors would like to thank the reviewers for their high-quality reviews, which provided insightful and constructive feedback to the authors of the papers.

This article presents a Light Intensity--based Positioning System (LIPS) for indoor environments. The system uses off-the-shelf light-emitting diode lamps as signal sources and light sensors as signal receivers. The design is inspired by the observation that a light sensor has deterministic sensitivity to both the distance and incident angle of a light signal, an under-utilized feature of photodiodes now widely found on mobile devices. We develop a stable and accurate light intensity model to capture the phenomenon, based on which a new positioning principle, Multi-Face Light Positioning , is established that uses three collocated sensors to uniquely determine the receiver’s position, assuming merely a single source of light. We have implemented a prototype on both dedicated embedded systems and smartphones. Experimental results show average positioning accuracy within 0.4m across different environments, with high stability against interferences from obstacles, ambient lights, temperature variation, and so on.

Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver coriders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this article, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide , in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox . In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers. To improve coRide ’s efficiency in mileage reduction, we formulate an NP-hard route calculation problem under different practical constraints. We then provide (1) an optimal algorithm using Linear Programming, (2) a 2-approximation algorithm with a polynomial complexity, and (3) its corresponding online version with a linear complexity. To encourage coRide ’s adoption, we present a win-win fare model as the incentive mechanism for passengers and drivers to participate. We test the performance of coRide by a comprehensive evaluation with a real-world trial implementation and a data-driven simulation with 14,000 taxi data from the Chinese city Shenzhen. The results show that compared with the ground truth, our service can reduce 33% of total mileage; with our win-win fare model, we can lower passenger fares by 49% and simultaneously increase driver profit by 76%.

Recent pioneer work has shown that packet receptions on adjacent links are correlated, which contradicts the long held assumption that wireless links are statistically independent. Since wireless link correlation affects a wide range of protocol designs, it is essential to quantify the impact generically. In particular, this paper focuses on a unified transmission cost metric for diversity-based routing schemes, including opportunistic routing, network coding, and hybrid routing. This paper covers both unicast and broadcast. Compared with the legacy metrics, our metric provides a direct and accurate estimation of the transmission cost in the presence of link correlation. The new metric helps a wide range of routing algorithms determine when they can benefit from reception diversity, and how to maximize the benefit, at negligible costs. We evaluate the metric on one 802.11 test bed and three 802.15.4 test beds running TelosB, MICAz, and GreenOrbs nodes. The experimental results show that our metric 1) reduces 92% and 94% of the estimation error of the transmission cost in unicast and broadcast and 2) outperforms the link independent metric in both unicast and broadcast applications.

Recent work has shown that wireless links are not independent, and that transmissions from a transmitter to multiple receivers are correlated. This finding has profound implications for the performance of network protocols such as broadcast, multicast, opportunistic routing, and network coding. In this paper, we show how link correlation can significantly impact broadcast. We present the design and implementation of CorLayer, a general supporting layer for energy-efficient reliable broadcast that carefully blacklists certain poorly correlated wireless links. The design uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of our work is CorLayer's broad applicability and effectiveness. We integrate CorLayer transparently with 16 state-of-the-art broadcast protocols specified in 13 publications on three physical testbeds running TelosB, MICAz, and GreenOrbs nodes, respectively. The experimental results show that CorLayer significantly improves energy efficiency across a wide spectrum of broadcast protocols and that the total number of packet transmissions can be reduced consistently by 47% on average.

As a supporting primitive of many mobile applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-to-peer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others’ devices. In this article, we propose Acc , which serves as an on-demand generic discovery accelerating middleware for many deterministic neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Further, we present a proactive online rendezvous maintenance mechanism, which is used to reduce delays for the detection of leaving of neighbors. Our evaluations show that Acc -assisted discovery schemes reduce latency by up to 51.8% compared to schemes consuming the same amount of energy. More importantly, to prove the real-world value of Acc , we further present and evaluate a Crowd-Alert application where Acc is employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 280GB dataset of more than 14,000 taxis in Shenzhen, the most crowded city in China.

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold Rt. We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a distributed algorithm with provable approximation ratio (1 - ε). Through extensive simulation and testbed experiments, we demonstrate that our (1 - ε)-approximation algorithm outperforms the Set-Cover algorithm by up to 23%, and has an average performance gain of 41.1% over the SCP algorithm in terms of the overall charging utility.

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology receives intensive interests, as it allows the power to be transferred from a charger to ambient devices wirelessly. The existing studies mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the Safe Charging Problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold Rt. We prove that SCP is NP-hard and propose a solution which provably outperforms the optimal solution to SCP with a relaxed EMR threshold (1 - ε)Rt. Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when ε = 0.1, while a naive greedy algorithm is 34.6% below our solution.

Detection about urban emergency events, e.g., The fires, storms, traffic jams is of great importance to protect the security of humans. While there are limited physical sensors such as surveillance cameras in a city, urban emergency events are still difficult to be detected for their real-time feature. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media often includes references to urban emergency events occurring at, or affecting specific locations. In this paper, the real-time detection of urban emergency events based on social media is proposed. Firstly, users of social media are set as the target of crowd sensing. Secondly, the spatial and temporal information from the social media are extracted to detect the real-time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive experiments based on five real urban emergency events. The result show the accuracy and efficiency of the proposed method.

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.

Coverage is a fundamental problem in wireless sensor networks (WSNs). Conventional studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a field of interest (FoI) is complex in many real-world applications. However, existing coverage studies do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the field of interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. Thus, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, what is the expected coverage ratio when a number of sensors are adopted? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct extensive simulations to evaluate the performance of the proposed algorithms.

Reducing transmission redundancy is key to efficient broadcast in wireless networks. A standard approach to achieving this goal is to create a network backbone consisting of a subset of nodes that are responsible for data forwarding, while other nodes act as passive receivers. On top of this, network coding (NC) is often used to further reduce unnecessary transmissions. The main problem with existing backbone and NC combinations is that the backbone construction process is blind of what is needed by NC, thus may produce a structure that limits the power of NC algorithms. To address this problem, we propose Coding Opportunity Aware Backbone (COAB) metrics, which seek to maximize coding opportunities when selecting backbone forwarders. We show that the backbone construction process guided by our metrics leads to significantly increased coding frequency, at the cost of minimal localized information exchange. The highlight of our work is COAB's broad applicability and effectiveness. We integrate the COAB metrics with ten state-of-the-art broadcast algorithms specified in eight publications [1]-[8], and evaluate COAB with a running testbed of 30 MICAz nodes and extensively simulations. The experimental results show that our design outperforms the existing schemes substantially.

In traditional radio-based localization methods, the target object has to carry a transmitter (e.g., active RFID), a receiver (e.g., 802.11 × detector), or a transceiver (e.g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow the tracking of a transceiver-free object. Based on radio signal strength indicator (RSSI), which is readily available in wireless communication, three centralized tracking algorithms, and one distributed tracking algorithm are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has higher tracking accuracy but requires calibration. The probabilistic cover algorithm is based on distributed dynamic clustering. It can dramatically improve the localization accuracy when multiple objects are present. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the localization accuracy for single object can reach about 0.8 m and for multiple objects is about 1 m.

Carpooling taxicab services hold the promise of providing additional transportation supply, especially in extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both regular and carpooling services, called CallCab, based on a data driven approach. In response to a passenger's request, CallCab aims to recommend either (i) a vacant taxicab for a regular service with no detour, or (ii) an occupied taxicab heading to the similar direction for a carpooling service with less detour, yet without assuming any knowledge of destinations of passengers already on occupied taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS datasets and context information collected in the existing taxicab infrastructure. To improve CallCab's efficiency to process such a big dataset, we augment the efficient MapReduce model with a Measure phase tailored for our application. We evaluate CallCab with a real world dataset of 14,000 taxicabs, and results show that compared to ground truth, CallCab can reduce 64% of the total mileage to deliver all passengers and 63% of passenger's waiting time.

Fault-tolerance is essential in extreme-scale data processing systems. Pro-active fault-tolerance scheme (such as the speculative execution in MapReduce framework), can dramatically improve the response time of job executions when the failure becomes norm rather than an exception. Efficient pro-active fault-tolerance schemes require precise knowledge on the task executions, which has been an open challenges for decades. To well address the issue, in this paper we design and implement RiskI, a profile-based prediction algorithm in conjunction with a risk-aware task assignment algorithm to accelerate task executions, taking the uncertainty nature of tasks into account. Our design demonstrates that the nature uncertain not only brings great challenges but also new opportunities. With a careful design, we can benefit from such uncertainties. We implement the idea in Hadoop 0.21.0 systems and the experimental results show that compared with the traditional LATE algorithm, the response time can be improved by 46% with the same system throughput.

Reducing transmission redundancy is key to the efficiency of wireless network broadcast. A standard technique to achieve this is to create a network backbone consisting of a subset of nodes that are responsible for data forwarding, while other nodes act as passive receivers. On top of this, network coding (NC) is often used to further reduce unnecessary transmissions. The main problem with this backbone+NC approach is that the backbone construction process is blind of what is needed by NC, thus may produce a structure with little benefit to the NC algorithms. To address this problem, we propose a Coding Opportunity Aware Backbone (COAB) construction scheme, which seeks to maximally exploit coding opportunities when selecting backbone forwarders. We show that the better informed backbone construction process leads to significantly increased coding frequency, at minimal cost of localized information exchange. The highlight of our work is COAB's broad applicability and effectiveness. We integrate COAB with ten state-of-the-art broadcast algorithms, specified in eight publications [1]-[8], and evaluate it with prototype implementations with 30 MICAz nodes. The experimental results show that our design outperforms the existing schemes substantially.

Radio Signal Strength (RSS) based ranging is attractive by the low cost and easy deployment. In real environments, its accuracy is severely affected by the multipath effect and the external radio interference. The well-known fingerprint approaches can deal with the issues but introduce too much overhead in dynamic environments. In this paper, we attempt to address the issue along a completely different direction. We propose a new ranging framework called Fredi that exploits the frequency diversity to overcome the multi-path effect solely based on RSS measurements. We design a Discrete Fourier Transformation based algorithm and prove that it has the optimal solution under ideal cases. We further revise the algorithm to be robust to the measurement noises in practice. We implement Fredi on top of the USRP-2 platform and conduct extensive real environments in indoor environments. Experimental results show the superiority performance compared with the traditional methods.

Wireless communication essentially occurs in a broadcast medium with concurrent receptions. Recent works [34, 41] have shown clear evidence that wireless links are not independent and that transmissions from a transmitter to multiple receivers are correlated, a phenomenon that has profound implications for the performance of network protocols such as broadcast, multi-cast, opportunistic forwarding and network coding. In this paper, we show how link correlation can significantly impact broadcast. We present the design and implementation of CorLayer, a general supporting layer for energy efficient reliable broadcast that carefully blacklists certain poorly correlated wireless links. This method uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of our work is CorLayer's broad applicability and effectiveness. Our system effort is indeed significant. We integrate CorLayer transparently with sixteen state-of-the-art broadcast protocols specified in thirteen publications [1, 3, 18, 19, 23, 25--27, 32, 36, 38--40] on three physical testbeds running TelosB, MICAz, and GreenOrbs nodes, respectively. The experimental results show that CorLayer remarkably improves energy efficiency across a wide spectrum of broadcast protocols and that the total number of packet transmissions can be reduced consistently by 47% on average.

Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multistation wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper, we propose a new communication model where the control frames can be "attachedâ to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 200 percent compared with the existing 802.11 family protocols.

The growing importance of operations such as identification, location sensing, and object tracking has led to increasing interests in contactless Radio Frequency Identification (RFID) systems. Enjoying the low cost of RFID tags, modern RFID systems tend to be deployed for large-scale mobile objects. Both the theoretical and experimental results suggest that when tags are in large numbers, most existing collision arbitration protocols do not satisfy the scalability and time-efficiency requirements of many applications. To address this problem, we propose Adaptively Splitting-based Arbitration Protocol (ASAP), a scheme that provides efficient RFID identification for both small and large deployment of RFID tags, in terms of time and energy cost. Theoretical analysis and simulation evaluation show that the performance of ASAP is better than most existing collision-arbitration solutions and the time efficiency is close to the theoretically optimal values.

Well-balanced workload among wireless access points (APs) in a wireless local-area network (WLAN) can improve the user experience for accessing the Internet. Most load balancing solutions in WLANs focuses on the optimization of AP operations, assuming that the arrivals and departures of users are independent. However, through the analysis of AP usage based on a real WLAN trace of one-month collected at the Shanghai Jiao Tong University (SJTU), we find that such an assumption does not hold. In fact, due to users' social activities which is particularly time for enterprise environments, they tend to arrive or leave in unison, which would disruptively affect the load balance among APs. In this paper, we propose a novel AP allocation scheme to tackle the load balancing problem in WLANs, taking into account the social relationships of users. In this scheme, users with intense social relationships are assigned to different APs so that jointly departure of those users would have minor impact on the load balance of APs. Given that the problem of allocating an AP for each user so that the average of the sums of social relation intensity between any pair of users in each AP is NP-complete, we propose an online greedy algorithm. Extensive trace-driven simulations demonstrate the efficacy of our scheme. Comparing to the state-of-the-art method, we can achieve about 64.7 percent balancing performance gain on average during peak hours in workdays.

Transceiver-free object tracking is to trace a moving object that does not carry any communication device in an environment with some monitoring nodes predeployed. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So, the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result, it can reach the tracking accuracy to around 1 m by just using three nodes in a triangle area with 4 m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26 m. Our system scales well to a large deployment field without sacrificing the latency and accuracy.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multi-station wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper we propose a new communication model where the control frames can be “attached” to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 72% compared with the existing 802.11 family protocols.

Among the various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is apt to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile in run-time. In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and reform it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is 1 meter. Compared with the traditional RSS-based approaches in dynamic environment, the accuracy improves up to 10 times.

In wireless mobile networks, energy can be saved by using dynamic transmission scheduling with pre-knowledge about channel conditions. Such pre-knowledge can be obtained via profiling as proposed by several existing systems which assumed that the existence of spatial link correlation makes the measured channel status at one location reusable over a long period of time. Our empirical data, however, tells a different story: spatial link correlation only maintains well within a short duration (from seconds to tens of seconds) while decreases significantly afterwards, a phenomena we call ephemeral link correlation. By leveraging this observation, we design and implement a real-time transmission scheduling system, named PreSeer, on the railway platform for cargo transportation, where the transmission of a sink (to cellular towers) can be scheduled intelligently by utilizing future channel status measured by sinks located in front of it on the same train. We have implemented and evaluated the PreSeer system on the collected data extensively over 7,000-kilometer railway routes during a period of one and half years. Results reveal that PreSeer can help save as much as 40% energy, comparing with three base-line algorithms. More importantly, lessons learned from this major effort provide useful guidelines for transmission scheduling in highly-dynamic mobile environments, where (i) channel measurements cannot be perfectly aligned due to varying vehicle velocities, and (ii) the accuracy of channel measurements is subject to hardware discrepancy as well as environment irregularity.

As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-to-peer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others' devices. In this paper, we propose Acc, which serves as an on-demand generic discovery accelerating middleware for many existing neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Our evaluations show that Acc-assisted discovery schemes reduce latency by a maximum of 51.8%, compared with the schemes consuming the same amount of energy. We further present and evaluate a Crowd-Alert application where Acc can be employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 10 GB dataset of more than 15,000 taxis in a metropolitan area.

Topology control is an effective method to improve the energy-efficiency and increase the communication capacity of Wireless Sensor Networks (WSNs). Traditional topology control algorithms are based on deterministic model that fails to consider lossy links which provide only probabilistic connectivity. Noticing this fact, we propose a novel probabilistic network model. We meter the network connectivity using network reachability. It is defined as the minimal of the upper limit of the end-to-end delivery ratio between any pair of nodes in the network. We attempt to find a minimal transmission power for each node while the network reachability is above a given application-specified threshold. The whole procedure is called probabilistic topology control (PTC). We prove that PTC is NP-hard and propose a fully distributed algorithm called BRASP. We prove that BRASP has the guaranteed performance and the communication overhead is O(|E| + |V|). The experimental results show that the network energy-efficiency can be improved by up to 250% and the average node degree is reduced by 50%.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference,” can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free” coordination channel can be built. Based on this idea, we propose a DC-MAC to leverage this "free” channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250 percent.

IEEE 802.15.4 standard specifies physical layer (PHY) and medium access control (MAC) sublayer protocols for low-rate and low-power communication applications. In this protocol, every 4-bit symbol is encoded into a sequence of 32 chips that are actually transmitted over the air. The 32 chips as a whole is also called a pseudonoise code (PN-Code). Due to complex channel conditions such as attenuation and interference, the transmitted PN-Code will often be received with some PN-Code chips corrupted. In this paper, we conduct a systematic analysis on these errors occurring at chip level. We find that there are notable error patterns corresponding to different cases. We then show that recognizing these patterns enables us to identify the channel condition in great details. We believe that understanding what happened to the transmission in our way can potentially bring benefit to channel coding, routing, and error correction protocol design. Finally, we propose Simple Rule, a simple yet effective method based on the chip error patterns to infer the link condition with an accuracy of over 96 percent in our evaluations.

Cooperative caching is an efficient way to improve the performance of data access in mobile wireless networks, by cache nodes cooperatively caching copies of data items in their limited storage. With more demands on sharing a video or other data, especially for mobile phone games in an Internet-based Mobile Ad Hoc Network, the relations among data items become much more important than before. However, most of the existing works do not consider inherent relations among these data items. In this paper, we present a novel solution, Gossip-based Cooperative Caching (GosCC), to address the cache placement problem, considering the sequential relation among data items. Each mobile node stores the IDs of data items cached locally and the ID of the data item in use into its progress report. Each mobile node also makes use of these progress reports to determine whether a data item should be cached locally. These progress reports are propagated within the network in a gossip-based way. To improve user experience, GosCC aims to provide users with an uninterrupted data access service. Simulation results show that GosCC achieves better performance than the best one of the existing solutions, Benefit-based Data Caching, in terms of average interruption intervals and average interruption times, while sacrificing message cost to a certain degree.

Transceiver-free object tracking is to trace a moving object without carrying any communication device in an environment where the environment is pre-deployed with some monitoring nodes. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result it can reach the tracking accuracy to around 1m by just using three nodes in a triangle area with 4m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26s. Our system scales well to a large deployment field without sacrificing the latency and accuracy.

Quorum-based schemes, e.g., GQS, can ensure asynchronous neighbors to discover each other within bounded time under low-duty-cycle operations. But they are tightly based on the assumption that duty cycles of devices fit some specific prefixed patterns. We tackle this problem by proposing a distributed quorum system to speed up the discovery.

Reliable communications are essential for most applications in wireless sensor networks (WSNs). In traditional approaches, the per-hop and end-to-end (E2E) recovery schemes are widely used. These schemes, however, suffer from low E2E success rate and poor energy efficiency in large-scale real environments. Through empirical studies, in this paper we identify three major problems that hinder the efficient and reliable communications. To address these problems, we propose a novel in-middle recovery scheme and realize it by designing and implementing a proliferation routing. Proliferation routing integrates three core technologies, namely, capability-based path finder, a randomized dispersity, and reproduction. Proliferation routing offers great flexibilities for transmissions. It cannot only be applied with any Medium Access Control (MAC) protocols and routing metrics, but also obtains a desired service quality (i.e., transmission success rate, energy cost, etc.) by controlling the system parameters. To demonstrate the effectiveness of proliferation routing, we thoroughly analyze its performance. We also conduct performance evaluations through implementation experiments as well as simulations. In a specific experimental setup, proliferation routing can increase the E2E transmission success rate up to 80 percent compared with the well-known hop-based routing and flooding.

Recent years have seen an increasing need of wireless networks in a mobile environment serving for more complex tasks and applications. Mobility becomes an indispensable factor of the system design and has been widely recognized as a general cause of packet loss. Though many works have been done on mobility study, to the best of our knowledge, they are mainly based on simulations or analytical studies that assume idealized link conditions. In this work, we experimentally investigate the nature of the error characteristics of mobility-induced packet losses at "chip-level" in IEEE 802.15.4. We believe this more understanding of mobility-induced packet losses can bring great potential benefits for further study on channel coding, routing and protocol design. Toward this end, we design and implement an efficient algorithm to distinguish mobility-induced packet losses from other packet losses of static environments such as attenuation. Our algorithm is greatly advantaged as it needs no training data even when environment changes. Collecting three corrupted packets is sufficient to obtain a satisfactory performance. This feature makes our design in particular suitable for dynamic and mobile environments, allowing real-time mobility induced loss detection in an online manner. Experiments based on GNU Radio testbed show that our algorithm can provide an accuracy of up to 96%.

The growing importance of operations such as identification, location sensing and object tracking has led to increasing interests in contact less Radio Frequency Identification (RFID) systems. Enjoying the low cost of RFID tags, modern RFID systems tend to be deployed for large-scale mobile objects. Both the theoretical and experimental results suggest that when tags are mobile and with large numbers, two classical MAC layer collision-arbitration protocols, slotted ALOHA and Tree-traversal, do not satisfy the scalability and time-efficiency requirements of many applications. To address this problem, we propose Adaptively Splitting-based Arbitration Protocol (ASAP), a scheme that provides low-latency RFID identification and has stable performance for massive RFID networks. Theoretical analysis and experimental evaluation show that ASAP outperforms most existing collision-arbitration solutions. ASAP is efficient for both small and large deployment of RFID tags, in terms of time and energy cost. Hence it can benefit dynamic and large-scale RFID systems.

Sensing coverage is essential for most applications in wireless networks. In traditional coverage problem study, the disk coverage model has been widely applied because of its simplicity. Though notable recent works point out that the disk model has many critical limitations when applied in practice, few successful works have been conducted to comprehensively study the issue. Motivated by this, in this paper we propose a new coverage model called T-R model. T-R model is derived from a real application of transceiver-free object detection. Compared with the traditional disk model, T-R model is able to describe many new coverage features such as the probabilistic coverage, the link-centric coverage units and the correlations between multiple coverage units. These new capabilities make T-R model a better abstraction of individual sensors. To evaluate the performance of T-R model, we conduct comprehensive empirical studies based on a test-bed of 30 telosB nodes. Experimental results show that the TR model can adequately describe the sensing behavior in the transceiver-free object detection applications. The average error between the model and the reality is only 8%. Moreover, T-R model presents attractive flexibility, making it more appropriate for general coverage problem studies than the transceiver-free object detection.

Spectrum sensing is essential for the success of the cognitive radio networks. In traditional spectrum sensing schemes, Secondary Users (SUs) are responsible for the spectrum sensing which could be very time and resource consuming. It leads to a great deal of inefficiency in spectrum usage and introduces many practical challenges. To tackle these challenges and leverage the spectrum opportunity more efficiently, we propose a new system that provides a spectrum sensing service for SUs using dedicated wireless spectrum sensor networks (WSSNs). In this paper we focus on the sensing channel assignment problem in WSSNs and formulate the problem as a Sensing Effectiveness Maximization Problem (SEMP). We prove that SEMP is NP-complete under the ideal case, and show that the more challenges arises in real environments. To address the issues, we systematically study the design tradeoff and critical factors when maximizing the sensing effectiveness. Based on these study results we propose a Sensing Channel Assignment algorithm (SCAS). We conduct test-bed empirical investigations as well as comprehensive simulations. Performance evaluation results show that for both the scenarios of given deployments and manual deployments, SCAS is able to sense more channels to improve the sensing effectiveness. The improvement is up to 300% and the average improvement is 150% compared with other simple alternatives.

Spectrum sensing is one of the key enabling technologies in Cognitive Radio Networks (CRNs). In CRNs, secondary users (SUs) are allowed to exploit the spectrum opportunities by sensing and accessing the spectrum, which exhibit many critical limitations in practical environments. In this paper, we propose a new sensing service model that uses dedicated wireless spectrum sensor networks (WSSN) for spectrum sensing. The major challenge in WSSN is the design of data fusion, for which the traditional fusion scheme will produce a large amount of errors. We formulate the problem as a boundary detection problem with notable unknown erroneous inputs. To solve the problem, we propose a novel cooperative boundary detection scheme that intelligently incorporates the cooperative spectrum sensing concept and the recent advances in support vector machine (SVM). Cooperative boundary detection consists of two major components, a declaration calibration algorithm and a boundary derivation algorithm. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. A prototype system as well as simulation experiments show that compared with the traditional approaches, cooperative boundary detection can reduce the errors by up to 95% with an average reduction about 85%.

Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique features, such as highly mobile environments, supremely limited size of sample objects, and the non-uniform, biased samples. All these features have raised new challenges that make the traditional density-based clustering algorithms fail to capture the real clustering property of objects, making the results less meaningful. In this paper we propose a novel, non-density-based approach called mobility-based clustering. The key idea is that sample objects are employed as "sensors" to perceive the vehicle crowdedness in nearby areas using their instant mobility, rather than the "object representatives". As such the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified and techniques to compensate these effects are proposed. We evaluate the performance of mobility-based clustering based on real traffic situations. Experimental results show that using 0.3% of vehicles as the samples, mobility-based clustering can accurately identify hot spots which can hardly be obtained by the latest representative algorithm UMicro.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that, coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference", can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free" coordination channel can be built. Based on this idea we propose a DC-MAC to leverage this "free" channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250%.

Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either "connected" or "disconnected." These approaches are called connectivity-based topology control. In real environments, however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Toward this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NP-hard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(\vert E\vert ), where E is the link set of the original topology, and the space requirement for individual nodes is O(d), where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 Berkeley Mica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to six times.

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers' route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace.

Topology control is an effective method to improve energy-efficiency and increase the capacity in Wireless Sensor Networks (WSNs). To fully characterize WSNs with lossy links, we propose a novel probabilistic network model. Under this model, we meter the network quality using network reachability defined as the minimal of the upper limit of the end-to-end delivery ratio between any pair of nodes in the network.We attempt to find a minimal transmitting power for each node while the network reachability is above a given application- specified threshold, called probabilistic topology control (PTC). We prove that PTC is NP-hard and propose a fully distributed algorithm called BRASP. We prove that BRASP has the guaranteed performance. Two rules that must be followed by any algorithm have been identified. We conduct both simulations and prototype implementations based an 18-TelosB-node test- bed. The experimental results show that the network energy- efficiency can be improved by up to 250%. The average node degree is reduced by 50% which will lead to a great benefit for the network capacity.

Coverage is a fundamental problem in Wireless Sensor Networks (WSNs). Existing studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a targeted Field of Interest is complex in many real world applications; and yet, existing studies on coverage do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the targeted Field of Interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. In this paper, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, how many sensors are needed to reach a certain expected coverage ratio? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct comprehensive simulations to evaluate the performance of the proposed algorithms.

In this paper, we study the trade-off between network throughput and fairness in a multi-channel enabled wireless sensor network (WSN). Traditional approaches attempt to solve the two problems in an isolated manner without a joint design. Our empirical studies show that solutions to these two problems cannot be simply combined. Away from the traditional belief, the number of channels in WSNs with Telosb sensor nodes operating at 2.4 GHz band can be up to 83 and the orthogonal channels can be up to 27. The switching overhead in terms of time and energy cost is relatively small. Furthermore, we observe a buffer wall phenomenon which is one of the main reasons causing network throughput degradation and unfairness. To strike a better trade-off between the network throughput and fairness, we design a novel multi-channel assignment algorithm, targeting at maximizing the minimal data sending rate. The key idea of the proposed algorithm is to level down the buffer wall so that the buffer usage of nodes can be evenly distributed. As such, the bandwidth of bottleneck nodes can be fully utilized and the unfairness due to the node locality can be removed. We prove that the achieved data sending rate is no less than 4/9 of the optimal rate in theory. Our experimental results show that the minimal data sending rate can be improved by up to 100% comparing with the existing work TMCP.

Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either "connected" or "disconnected". These approaches are called connectivity-based topology control. In real environments however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Towards this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NPhard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(jEj) where E is the link set of the original topology, and the space requirement for individual nodes is O(d) where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 BerkeleyMica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to 6 times.

This paper presents the design, implementation and performance evaluation of a new MAC protocol, called A- MAC, for wireless sensor networks. A-MAC combines the strengths of TDMA and CSMA to achieve the goal of low power transmissions for long-term surveillance and monitoring applications, where sensor nodes are typically vigilant for a long time and inactive most of the time until some event is detected. A-MAC employs an advertisement mechanism to eliminate collisions and reduce the overhearing and idle listening, which are the major energy wastes in wireless sensor networks. The distinctive feature of A-MAC is that a node needs to be active only when necessary as it is the transmitter or the receiver. During other times it can safely turn off its radio. Furthermore, to meet different application requirements, A-MAC supports two operation modes by which nodes can adaptively switch their operation modes according to the instant requirements and conditions of the network. A-MAC is implemented in TinyOS. By comparing A-MAC with existing MAC protocols, we show that A-MAC presents significant improvements in terms of power consumption and throughput.

Having an ID being unique within the application domain for each sensor node is the basic assumption in many WSN applications. In traditional assignments, fixed-length ID schemes, which are severely limited by scalability, flexibility and energy efficiency, are adopted. To solve these problems, we propose a novel variable-length ID scheme that allows different nodes to have different ID lengths. To realize this idea, we propose a fully localized ID assignment algorithm called emphself-organized ID assignment (SIDA). SIDA presents great potential on scalability, flexibility and energy efficiency by enabling online ID assignment and various optimizations according to different communication paradigms. Analytical and simulation results show that SIDA has a comparable static ID length while the accumulated communication overhead for multi-hop transmissions can be saved by 20%; the control overhead of the new deployment assignment can be reduced to about 30% compared with fixed-length schemes.

Reliable transmission service is in dire need for many applications in wireless sensor networks (WSNs). Most existing routing protocols however, seriously suffer from low end-to-end success rates in real deployments. Through extensive experiments on a test-bed of Mica2 nodes, we identify three key problems that hinder the reliable packet delivery. In order to address these problems and therefore to provide a reliability-oriented transmission service, we propose a novel in-middle recovery mechanism that fills the gap between the traditional per-hop recovery and end-to-end recovery mechanisms. To realize such an idea, we design and implement proliferation routing that leverages randomized dispersity and reproduction. The distinctive feature of proliferation routing is its great flexibility. Not only can it be applied with any medium access control (MAC) protocol and routing metric, but also a desired service quality can be effectively derived by controlling the system parameters. Such a feature is revealed by theoretical analysis and confirmed by implementation and simulation experiments. In a specific experiment setup, proliferation routing can increase the end-to-end transmission success rate up to 70% compared with the well-known hop-based routing and flooding.

In wireless mobile ad hoc networks (MANETs), energy is a crucial resource. Topology control technology allows network nodes to reduce their transmission power while preserving the network connectivity. A MANET is a non-cooperative system so that only when a node earns its payment, which can cover its cost, the cooperation can be stimulated. We design a truthful topology control mechanism (TRUECON) for MANETs to induce the selfish, but rational, network nodes to collaborate. Truth-telling is a dominant strategy in TRUECON. A node needs to reveal its true value in order to obtain the maximum expected utility. We prove the overpayment of TRUECON has a bound depending on different radio propagation models

Due to critical resource constraints, associating each sensor node with a global ID is not feasible in wireless sensor networks (WSNs). However, having an ID in each sensor node is the basic assumption in many WSN applications. Therefore, efficient local ID generation schemes, which have not been carefully considered in the past, is of great importance. In this paper, we propose a novel local ID assignment scheme, called GREENWIS. Without having the explicit location information in advance, each node is able to generate a 5-tuple local ID within an application field in a distributed manner using the GREENWIS algorithm. We prove the correctness and show the effectiveness of GREENWIS by comprehensive analysis. A GREENWIS prototype is also deployed in our lab. We introduce our early experience with this design and implementation. We evaluate GREENWIS through intensive experiments. The results show that GREENWIS provides effective local ID assignment in a real WSN environment