Simultaneous Localization and Mapping (SLAM) has emerged as a cornerstone technology in intelligent transportation systems (ITS) and autonomous robots, finding widespread applications in various scenarios and multiple platforms autonomous driving tasks. However, in environments with weak textures and motion blur, achieving efficient and robust visual-inertial SLAM remains challenging. Current research utilizes line features to enhance SLAM performance in such environments, but incurs difficulties in line extraction, structured scene representation, and computational overheads involved in joint optimization. To address these challenges, this paper introduces a novel SLAM framework with both high pose estimation accuracy and high back-end computational efficiency. Firstly, an intelligent spatial line integration method is proposed to effectively reduce data redundancy in joint optimization by leveraging the remarkable stability of long line segments in structured scenes, thereby enhancing the spatial consistency of structural lines. Secondly, to combat low-light and high-speed motion environments, an optical flow tracking accuracy verification method is designed to bolster the system’s tracking performance and robustness in complex scenarios. Finally, to relieve the substantial computational overhead arising from high-dimensional optimization parameters in bundle adjustment (BA), a multi-mechanism marginalization strategy is presented to enhance the accuracy and computational efficiency of BA, while also preventing scale explosion. Comparative evaluations against state-of-the-art algorithms on both the EuRoC MAV and TUM VI benchmark datasets demonstrate that the proposed framework significantly improves localization accuracy and joint optimization efficiency.

WiFi probes are crucial tools in applications such as crowd analysis and traffic monitoring. Due to indiscriminate sniffing of WiFi packets in the environment, probes generate a large amount of irrelevant data, increasing server-side processing overhead and presenting challenges for efficient data collection and real-time information processing. Moreover, a single wired transmission mode significantly limits the application scope and deployment efficiency of probes. In response, this paper presents a lightweight WiFi probe system combining a dual transmission mode with device classification capability. First, the system hardware, composed of a single STM32 and multiple ESP8266 modules, enables both wired and wireless data transmission modes through embedded programming. Second, a lightweight logistic and decision fusion model (LLDFM) is proposed, utilizing a time window and device library dynamic management mechanism to achieve real-time classification of fixed/temporary WiFi devices, which can effectively distinguish and discard application-irrelevant fixed device data. Finally, a server web platform was developed with real-time data sniffing, storage, multi-probe monitoring, and management functions to evaluate the system's practical performance. Results from extensive real-world experiments demonstrate an average fault-free operational time of over 168 hours, a fixed/temporary WiFi device classification accuracy of 97.19 %, and the ability to filter 30.45 % of irrelevant data, reducing the corresponding data transmission volume. These findings confirm the proposed system's reliability, efficiency, and superiority.

Recent progress in crowd counting has underscored its potential across diverse real-world applications. Nevertheless, the majority of existing approaches remain constrained by reliance on unimodal data, thereby limiting both robustness and generalizability. To address these challenges, we present a novel virtual simulation framework for generating synchronized multi-modal synthetic datasets. The proposed framework supports automated data generation with high-fidelity ground-truth annotations and provides programmatic control over environmental conditions and crowd dynamics. Through comprehensive experiments employing pre-training and fine-tuning strategies, we demonstrate that the synthetic datasets produced by our framework substantially improve model performance and generalization in crowd counting tasks. The work contributes a scalable and reproducible solution to the problem of data scarcity in multi-modal crowd counting research.

Real-time awareness of crowd counts and distribution is vital for applications like crowd management, traffic control, and urban planning. Compared to vision-based methods, passive Wi-Fi sensing-based approaches offer advantages, such as lower deployment costs, broader coverage, and greater scalability, and thus have become prevalent for fine-grained crowd analysis, e.g., estimating mobile device locations and subsequently inferring crowd density maps (CDMs). Therein, substantial localization errors are often incurred due to significant measurement noises, and even worse, the sporadic active scanning introduces sparsity in localization-based density maps (DMs), degrading the accuracy of resulting crowd analysis. To address these challenges, Wi-FDM, a passive Wi-Fi sensing-based framework for fine-grained CDM estimation, is proposed. Specifically, to mitigate the considerable localization errors, two models based on convolutional neural networks are employed to capture the relationship between received signal strength fingerprints and various locations more effectively; to alleviate the sparsity in localization-based DMs, deep compressed sensing networks are developed to reconstruct fine-grained CDMs by recover missing spatial details; to efficiently transfer the CDM reconstruction capabilities developed on semi-synthetic datasets to diverse real-world scenarios, learnable linear and convolutional residual blocks were designed to establish correlations across environments, addressing variations in scene characteristics and improving model generalization. Experimental results on real-world datasets demonstrate Wi-FDM not only surpasses state-of-the-art methods by 41.8% and 29.3% in crowd counts and distribution estimation but is also applicable to both indoor and outdoor scenes, demonstrating its potential for crowd management and analysis.

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs.

Popular vision-based crowd counting methods suffer from huge costs, limited coverage and high complexity, making it difficult to be applied for large surveillance areas, while emerging WiFi-based methods which are suitable for large surveillance areas incur limited accuracy due to the sparsity and randomness of WiFi sniffing data. Considering the fact that the variations of audio data are spatial-temporally correlated with crowd fluctuations, this paper proposes to fuse audio and WiFi sniffing data for crowd counting by developing a Cross-modal Multi-level Perception Network, termed CMPN. The CMPN can not only extract crowd features from the bimodal data to leverage the temporally continuity for compensating sparse WiFi sniffing data, but also mine the correlation of intra- and inter-modality crowd features for accurate crowd counting. Extensive experiments are conducted in a real campus with the surveillance area of about 4000m2, and demonstrate that the CMPN can achieve the mean absolute error of 5.88, resulting in a 22.12% reduction compared to the state-of-the-art WiFi-only method.

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction; to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds; to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas.

Crowd counting aims to estimate the number of individuals in targeted areas. However, mainstream vision-based methods suffer from limited coverage and difficulty in multi-camera collaboration, which limits their scalability, whereas emerging WiFi-based methods can only obtain coarse results due to signal randomness. To overcome the inherent limitations of unimodal approaches and effectively exploit the advantage of multi-modal approaches, this paper presents an innovative WiFi and video-fused multi-modal paradigm by leveraging a heterogeneous dual-attentional network, which jointly models the intra- and inter-modality relationships of global WiFi measurements and local videos to achieve accurate and stable large-scale crowd counting. First, a flexible hybrid sensing network is constructed to capture synchronized multi-modal measurements characterizing the same crowd at different scales and perspectives; second, differential preprocessing, heterogeneous feature extractors, and self-attention mechanisms are sequentially utilized to extract and optimize modality-independent and crowd-related features; third, the cross-attention mechanism is employed to deeply fuse and generalize the matching relationships of two modalities. Extensive real-world experiments demonstrate that our method can significantly reduce the error by 26.2%, improve the stability by 48.43%, and achieve the accuracy of about 88% in large-scale crowd counting when including the videos from two cameras, compared to the best WiFi unimodal baseline.

Great efforts have been devoted to solving the crowd counting problem based on vision or other fine-grained measurements. Popular vision and WiFi channel state information based approaches, though are able to achieve relatively high accuracy, suffer from limited scalability. In contrast, passive WiFi sensing-based approaches are capable of supporting large surveillance areas, but often rely on certain global linear or approximately linear regression models, which cannot accurately capture the complex mapping relationship between WiFi sensing data and the corresponding crowd count, especially in a large surveillance area during a long period of time. This paper addresses the issue from the following three aspects. Firstly, in order to combat with these coarse-grained regression models, the large surveillance is partitioned into grids, such that either a local linear model or other implicit local models can be built with respect to each grid. Secondly, sequential WiFi spatial-temporal matrix (SWSTM) is defined in alignment with grids to encode the spatial-temporal information of crowds based on passive WiFi localization and a sliding time window mechanism. Thirdly, the spatial-temporal correlations among crowd features of different grids are mined to better regress such local models by using a recurrent neural network (RNN) with SWSTMs as inputs. Extensive experiments are conducted in a real campus road network with an area of about$4000m^{2}$, and demonstrate that the proposed method significantly reduces the counting error rate from 22.54% to 13.44% compared to several state-of-the-art methods.

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding received signal strengths (RSSs) for the use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring across various mobile devices. In this article, we present a unified deep neural network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer–Rao lower bound (CRLB)-based analysis reveals that utilizing multichannel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel maximum likelihood estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world data sets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8%, respectively.

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning technologies had emerged in the past decades, and WiFi-based approach is one of the most promising ones. However, the existing WiFi localization methods fail to take into account the disparate influence of packets transmitted in different channels so as to inhibit the further improvement of localization accuracy. Therefore, we present CADNN: a Channel Adaptive WiFi localization method based on Deep Neural Network (DNN). Specifically, a comprehensive analysis on signal attenuations in different channels along with error analysis based on Cramer-Rao Lower Bound (CRLB) is conducted in theory. Then, the channel set splitting scheme for practical localization to leverage multi-channel features is proposed. Finally, we design a localization framework using multi-objective regression DNN to adapt Received Signal Strength (RSS) measurements from different channel sets. The results from real-world experiments confirm the effectiveness of channel adaptive and show that CADNN can improve localization accuracy by at least 25.3% and 19.5% respectively on the two datasets and it can serve thousands users within one second.

With the rapid development of the Internet and combined with outsourcing, a new paradigm - crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers' bidding.