The structure of topology underpins much of the research on performance and robustness, yet available topology data are typically scarce, necessitating the generation of synthetic graphs with desired properties for testing or release. Prior diffusion-based approaches either embed conditions into the diffusion model, requiring retraining for each attribute and hindering real-time applicability, or use classifier-based guidance post-training, which does not account for topology scale and practical constraints. In this paper, we show from a discrete perspective that gradients from a pre-trained graph?level classifier can be incorporated into the discrete reverse diffusion posterior to steer generation toward specified structural properties. Based on this insight, we propose Classifier-guided Conditional Topology Generation with Persistent Homology (CoPHo), which builds a persistent homology filtration over intermediate graphs and interprets features as guidance signals that steer generation toward the desired properties at each denoising step. Experiments on four generic/network datasets demonstrate that CoPHo outperforms existing methods at matching target metrics, and we further validate its transferability on the QM9 molecular dataset. The code is available at https://github.com/Lrbomchz/CoPHo.

The rapid proliferation of IoT-enabled cameras has driven increasing demand for low-latency, intelligent video understanding in real-world applications such as smart cities and industrial automation. While Multimodal Large Language Models (MLLMs) offer unprecedented capabilities in semantic reasoning and natural language-based video comprehension, their deployment in latency-sensitive IoT environments remains challenging due to high computational costs and sequential decoding bottlenecks. Moreover, conventional edge-cloud video analysis frameworks often rely on semantic-agnostic frame sampling, leading to information loss or redundant data transmission. In this paper, we proposeSEC, a semantic-aware edge-cloud collaborative framework for efficient and accurate video analysis.SECintroduces a task-aware key frame selection mechanism at the edge to maximize semantic relevance while minimizing bandwidth usage, and a novel adaptive speculative decoding framework with tree-based parallel generation on the cloud to accelerate MLLM inference. Extensive experiments under realistic edge-cloud deployment settings on four video understanding benchmarks demonstrate that the proposedSECframework achieves superior performance, significantly reducing the end-to-end inference latency while improving the accuracy, a rare win-win in latency-critical IoT systems.

Task scheduling is a critical component in edge computing. Many advanced scheduling strategies select the most suitable execution node based on user-reported resource requirements, thereby enhancing user experience. However, the reliance on user-reported resource demands presents a potential vulnerability, as malicious users could exploit this to disrupt edge servers. In this paper, we propose a hypothetical offloading attack scenario in which an attacker submits tasks with falsified resource demands, directing these tasks to specific nodes. A high volume of malicious tasks leads to congestion at the targeted node, causing delays and potential failures for latency-sensitive tasks. Due to the black-box nature of the offloading strategy and the unknown system environment, we model the task parameter manipulation as a multi-dimensional multi-armed bandit (MAB) process with non-stationary rewards. This approach aims to maximize the attack efficiency within limited attack costs. We introduce UCB algorithm for Multidimensional space with Cost constraints and Non-stationary rewards(UCB-MCN), an extended MAB algorithm designed to optimize the attacker's cumulative reward. In a simulated environment, UCB-MCN is tested against various offloading strategies and defense mechanisms. Evaluation results demonstrate the effectiveness of UCB-MCN and highlight new security concerns within edge computing.

Distributed training has become a cornerstone of large-scale deep learning, yet gradient synchronization remains a critical bottleneck—particularly in high packet loss environments such as wide-area networks (WANs), where frequent retransmissions lead to long-tail latency and degraded training performance. In this paper, we propose a novel approach that leverages spatiotemporal gradient importance modeling to optimize both the transmission protocol and switch queue dropping strategy. Our method dynamically evaluates the importance of each gradient based on its position in the model layers and the current training stage, enabling adaptive packet loss control. In terms of implementation, we integrate a hybrid UDP/TCP transmission protocol: UDP ensures high-throughput gradient delivery, while TCP carries control signals to provide reliable loss feedback. Additionally, our system uses DSCP marking to map gradients to different switch queues, with each queue configured with a distinct WRED (Weighted Random Early Detection) loss threshold that is periodically updated based on the dynamically assessed gradient importance. Extensive experiments demonstrate that our model effectively captures the intrinsic spatiotemporal variations in gradient behavior and significantly reduces long-tail latency while maintaining model accuracy. This provides a robust framework for optimizing gradient synchronization and network resource allocation in distributed deep learning systems.

Segment Routing over IPv6 (SRv6) is a promising source routing solution with wide-ranging applications in the field of Traffic Engineering. By adding SR tags into IPv6 packets, traffic can be directed to various SR segments, effectively distributing traffic. However, upgrading all network nodes to SRv6 nodes simultaneously is impractical. This paper addresses the incremental deployment of SRv6 from traffic engineering, aiming to minimize MLU within the network. We introduce the theory of celestial equilibrium and model the problem as a celestial equilibrium-like model with a global distribution of SR nodes and their corresponding areas of influence. To address this problem model, we propose a novel algorithm based on the EM algorithm, EM-SRTE. In our proposed framework, step E leverages a reinforcement learning algorithm combined with a self-attention module for graph learning to optimize the selection of SR nodes. Meanwhile, step M utilizes a similar algorithm to optimize the region range of SR nodes. Experimental results using publicly available datasets demonstrate that our model outperforms state-of-the-art baselines.

Advancements in distributed machine learning have led to a demand for engineers with knowledge in AI, hardware, and networking. Research aims to automate deployment based on user intent to improve development efficiency. Large Language Models (LLMs) assist in translating user intent for resource allocation but face challenges in translation accuracy and contextual integrity. The Chain-of-Thought (CoT) improves LLM reasoning by breaking down problems into sequential steps. However, the reasoning steps and dependencies between the model requirements and the computation and network configurations are complex, requiring the selection of an appropriate thought path to construct the CoT framework. To address these issues, we propose a computation and network resource deployment intent translation framework based on CoT reasoning and create a benchmark for user intent in distributed learning. This framework translates user intent into hardware and network configurations, using LLMs to optimize translation accuracy through logical reasoning. Experiments on four LLM models show significant accuracy improvements with CoT compared to traditional prompts. The feasibility of our framework has been validated through its implementation and testing on a real-world testbed.

Large-scale incast traffic generated by AI training tasks poses significant challenges to RDMA networks. Existing congestion control mechanisms, such as Artificial Intelligence ECN (AI ECN) and fixed-ratio PFC, struggle to mitigate frequent PFC pauses caused by ECN's delayed feedback. This paper proposes DSCC, a synergistic algorithm of PFC and ECN. Based on the AI ECN algorithm, DSCC adjusts the PFC threshold according to the incast degree. The adjustment process adheres to the threshold constraints of PFC and ECN. Experiments show that DSCC can significantly improve network performance.

The increasing demand for resource-efficient AI training has positioned Mobile Edge Computing (MEC) as a pivotal component in distributed machine learning tasks. Traditional scheduling methods, however, often fail to account for the inherent characteristics of training tasks, such as periodicity and task correlation, leading to sub-optimal resource utilization. To address these limitations, we propose the Multi-Graph to Graph Scheduler (MG2GS), a novel framework designed to optimize resource allocation and scheduling in MEC environments. MG2GS employs a graph neural network-based feature extractor to capture the spatiotemporal availability of network resources, while a reinforcement learning-based scheduler ensures optimal task scheduling decisions. By incorporating task structure and long-term resource distribution, MG2GS enhances resource utilization by more than 20% compared to existing methods. Simulation results demonstrate its effectiveness in increasing the number of scheduled tasks and improving overall resource efficiency for distributed AI model training.

Recently, prompt-based methods have emerged as a new alternative ‘parameter-efficient fine-tuning’ paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen. However, despite achieving notable results, existing prompt methods mainly focus on ‘what to add’, while overlooking the equally important aspect of ‘where to add’, typically relying on the manually crafted placement. To this end, we propose a region-based Adaptive Visual Prompt, named AdaViPro, which integrates the ‘where to add’ optimization of the prompt into the learning process. Specifically, we reconceptualize the ‘where to add’ optimization as a problem of regional decision-making. During inference, AdaViPro generates a regionalized mask map for the whole image, which is composed of 0 and 1, to designate whether to apply or discard the prompt in each specific area. Therefore, we employ Gumbel-Softmax sampling to enable AdaViPro’s end-to-end learning through standard back-propagation. Extensive experiments demonstrate that our AdaViPro yields new efficiency and accuracy trade-offs for adapting pre-trained models.

Recently, transformer models have demonstrated superior performance in video tasks. However, a prevalent limitation in most current video Transformers lies in their tendency to overlook inherent temporal regions of interest, such as motion trajectories, leading to susceptibility to redundant information during temporal modeling. Existing methods that pay attention to motion trajectories have high computational demands, lacking in lightweight efficiency. To strike a balance between effective modeling of temporal regions of interest and computational efficiency, we propose a video transformer backbone with deformable temporal attention (DTA). Inspired by the work on deformable receptive fields, DTA employs a lightweight decision network to enhance the flexibility of temporal attention. The decision network computes the offsets of tokens in the input feature map, enabling them to move to temporally relevant regions of interest and efficiently model temporal information. We conducted extensive experiments on three popular datasets and surpassed the baseline. Additionally, we performed ablation experiments specifically targeting the model structure and parameters. These results confirm the effectiveness of the proposed deformable temporal attention mechanism.

Video scene detection, an initial step of video analysis, temporally divides heterogeneous video into semantic segments, which is widely used in video summarization, search, browsing and retrieval. Video scene detection always cuts video into shots first and then groups these shots into segments. In this process, how to solve complex dependency relationship among shots is a barrier. The existing methods consider using Recurrent Neural Networks and Hidden Markov Model to simulate the dependency relationship between shots. However, linear approaches work a little on hierarchical video structure. In this paper, a GNN-based network is proposed to model complex structures of videos instead. Besides, the semantic gap between low-level features and high-level semantics is also a big obstacle of video scene detection. Here, three visual semantic elements in shot, i.e., environment, object, action and audio feature are extracted as shot representation. Later, we utilize a multi-modal fusion strategy, which combines early fusion and late fusion, to bridge the semantic gap between low-level features and high-level semantics. The proposed method was evaluated on BBC Planet Earth dataset and Open Video Scene Detection (OSVD) dataset, the experimental results demonstrate that the proposed method outperforms the state-of-the-art in video scene detection task.

Vision-Language Models (VLMs) built on contrastive learning, such as CLIP, demonstrate great transferability and excel in downstream tasks like zero-shot classification and retrieval. To further enhance the performance of VLMs, existing methods have introduced additional parameter modules or fine-tuned VLMs on downstream datasets. However, these methods often fall short in scenarios where labeled data for downstream tasks is either unavailable or insufficient for fine-tuning, and the training of additional parameter modules may considerably impair the existing transferability of VLMs on open-set tasks. To alleviate this issue, we introduce WaveDN, a wavelet-based distribution normalization method that can boost the VLMs' performance on downstream tasks without parametric modules or labeled data. Initially, wavelet distributions are extracted from the embeddings of the sampled, unlabeled test samples. Subsequently, WaveDN conducts a hierarchical normalization across the wavelet coefficients of all embeddings, thereby incorporating the distributional characteristics of the test data. Finally, the normalized embeddings are reconstructed via inverse wavelet transformation, facilitating the computation of similarity metrics between the samples. Through extensive experiments on two downstream tasks, using a total of 14 datasets covering text-image and text-audio modal data, WaveDN has demonstrated superiority compared to state-of-the-art methods.

Masked image modeling (MIM), as a self-supervised learning paradigm in computer vision, has gained widespread attention among researchers. MIM operates by training the model to predict masked patches of the image. Given the sparse nature of image semantics, it is imperative to devise a masking strategy that steers the model towards reconstructing high-semantic regions. However, conventional mask strategies often miss these high-semantic regions or lack alignment with the masks and semantics. To solve this, we propose the Global Patch-wise Attention (GPA) framework, a transferable and efficient framework for MIM pre-training. We observe that the attention between patches can be the metric of identifying high-semantic regions, which can guide the model to learn more effective representations. Therefore, we firstly define the global patch-wise attention via vision transformer blocks. Then we design the soft-to-hard mask generation to guide the model gradually focusing on high semantic regions identified by GPA (GPA as a teacher). Finally, we design an extra task to predict GPA (GPA as a feature). Experiments conducted under various settings demonstrate that our proposed GPA framework enables MIM to learn better representations, which benefit the model across a wide range of downstream tasks. Furthermore, our GPA framework can be easily and effectively transferred to various MIM architectures.

In the age of 5G and the advent of 6G technology, meeting various applications' diverse and stringent QoS requirements has gained greater urgency. Most existing works only address this problem by optimizing network resource allocation or routing generation, ignoring their mutual influence. In this paper, we jointly optimize resource allocation and routing generation while comprehensively considering the mutual influence and demonstrating that joint optimization is a bilevel optimization problem. We propose BIJO, consisting of two-level agents, to explore using DRL to address the bilevel optimization problem. The upper agent allocates resources for different types of services and the lower agents choose the forwarding path for each type of service. The two-level agents are optimized interactively and iteratively. Extensive experiments on Mininet confirm that BIJO provides a win-win outcome, where various requirements are met as much as possible and network resources are utilized evenly.

At present, with the rapid advancement of mul-timedia technology, more and more multimodal applications have emerged. The data communication of multimodal applications involves multiple modalities, and the transmitted data has deadline requirements and block transmission characteristics. However, existing transport layer protocols cannot meet the transmission requirements of applications by perceiving the data attributes and it is difficult to avoid high-priority modalities from excessively seizing transmission resources, resulting in transmission starvation in other modalities. Therefore, this paper considers the data blocks and their transmission requirements of the upper layer application as independent data objects and proposes an object multipath transmission scheduling al-gorithm (OMTS) that can consider the fairness of multimodal transmission. OMTS uses reinforcement learning algorithms to comprehensively consider the transmission requirements of data objects and the quality of multipath network transmission, to determine the transmission order and path allocation strategy of objects. In addition, we also design a model structure that separates scheduling and learning, allowing the algorithm to learn more valuable scheduling strategies through continuous interaction with the environment. The comparative experiment of data transmission through the network simulation environment shows that OMTS is superior to existing scheduling algorithms.

Video popularity prediction, also known as predicting the future popularity of a video, is a task that generally uses various modalities of the video, such as visual, audio, text, and metadata, to make predictions. However, the traditional approach puts all the modalities directly into a model for processing, which introduces noise and redundancy into the model. To address this issue, we propose Cost-effective Modality Selection(CeMS) for video popularity prediction, which can adaptively select cost-effective modalities as input based on the features of the metadata. Specifically, a policy network first obtains the prior information from metadata to analyze the differences in representation ability and computation cost between various modalities. Then, with the policy estimation, an optimal set of cost-effective modalities is determined, and the backbone network corresponding to the selected modalities is activated for feature extraction. Finally, the semantic information of all selected modalities is fed into the decoder to yield the predicted popularity. Experiments demonstrate that our proposed approach yields 63%-69% reduction in computation when compared to the traditional baseline that simply uses all the modalities. Additionally, we achieve consistent improvements in accuracy over the state-of-the-art methods.

Recent adaptive methods for efficient video recognition mostly follow the two-stage paradigm of "preview-then-recognition" and have achieved great success on multiple video benchmarks. However, this two-stage paradigm involves two visits of raw frames from coarse-grained to fine-grained during inference (cannot be parallelized), and the captured spatiotemporal features cannot be reused in the second stage (due to varying granularity), being not friendly to efficiency and computation optimization.To this end, inspired by human cognition, we propose a novel recognition paradigm of "View while Moving" for efficient long-untrimmed video recognition.In contrast to the two-stage paradigm, our paradigm only needs to access the raw frame once.The two phases of coarse-grained sampling and fine-grained recognition are combined into unified spatiotemporal modeling, showing great performance.Moreover, we investigate the properties of semantic units in video and propose a hierarchical mechanism to efficiently capture and reason about the unit-level and video-level temporal semantics in long-untrimmed videos respectively.Extensive experiments on both long-untrimmed and short-trimmed videos demonstrate that our approach outperforms state-of-the-art methods in terms of accuracy as well as efficiency, yielding new efficiency and accuracy trade-offs for video spatiotemporal modeling.

Since the advent of the Internet, IP addresses have been the core of the Internet. However, with the rapid development of the Internet in recent years, IP addresses are facing more and more problems, such as address exhaustion, low packet efficiency and low flexibility. The reason is that IP addresses use a fixed-length design and lack extensibility. The New IP network architecture and addressing method were born to solve these problems. Based on this architecture, the addressing scheme adopts variable-length and structured addresses. The address space can be smoothly expanded according to the network scale without modifying the old network address configuration. But there are some challenges about New IP, and the greatest one lies in the route lookup of variable-length IP addresses. Content Addressable Memories (CAMs) are widely used in high speed routers to find matching routes for packets in a routing table. They enable the longest prefix matching on fixed-length addresses to be completed in a single clock cycle. However, they can not deal with New IP prefixes with variable lengths directly. In this paper, we propose a mechanism using Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) to efficiently store New IP addresses and complete a route lookup in constant time. Moreover, we combine the hash scheme and CAMs matching scheme to shorten the extremely long New IP addresses and reduce TCAM storage space consumption. The simulation results show that our mechanism can provide high speed route lookup with low power consumption.

Social online video has emerged as one of the most popular application, where “bullet screen comment” is one of the favorite features of Asian users. User behavior report finds that most people are used to quickly navigate and locate his concerned video clip according to its corresponding video labels. Traditional scene segmentation algorithms are mostly based on the analysis of frames, which cannot automatically generate labels. Since time-synchronized comments can reflect the episode of current moment, this paper proposed an unsupervised video episode boundary detection model (VEBD) for bullet screen comment video. It could not only automatically identify each episode boundary, but also detect the topic for video tagging. Specifically, a Joint Episode-Topic model is first constructed to detect the hidden topic in initial partitioned time slices. Then, based on the detected topics, temporal and semantic relevancy between adjacent time slices are measured to refine the boundary detection accuracy. Experiments based on real data show that our model outperforms the existing algorithms in both boundary detection and semantic tagging quality.

Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.

Due to the low cost, high bandwidth and compatibility of Ethernet, Ethernet-based fronthaul has been utilized to carry sampled radio frequency (RF) signals from radio equipment (RE) to the radio equipment controller (REC). Meeting the stringent performance requirements regarding jitter for the Common Public Radio Interface (CPRI) over Ethernet is challenging. In this paper, a time division multiplexing (TDM) slot-based scheduling (TSS) algorithm is proposed to minimize the jitter; this algorithm consists of two main modules: slot greedy allocation and low-delay collaboration. Specifically, low-complexity slot greedy allocation aims to solve the NP-hard problem caused by the optimal slot allocation, and the low-delay collaboration among different switches is employed to avoid the large delays caused by a strict slot assignment. In the Ethernet-based fronthaul network, the simulation results demonstrate that, compared with the conventional benchmark algorithm, the TSS algorithm ensures lower jitter, and this significant decrease is achieved without incurring a large delay.

Time-sensitive Networking (TSN) has been extensively utilized to enable the transport of time-sensitive fronthaul flows in Ethernet-bridged networks. However, recently emerged ultra-low delay and jitter requirements of the TSN pose great challenges to Common Public Radio Interface (CPRI) over Ethernet. Considering this, we propose a Deterministic Transmittable Time-Based Asynchronous Scheduler (DTT-BAS) in this paper. The DTT- BAS uses the deterministic transmittable timestamp as the core scheduling mechanism, which numerically compares the timestamps to optimize the flow scheduling in the network. For the fronthaul scenario, simulation results validate that the DTT-BAS achieves a high probability of zero jitter and reduces the influence of clock skews with a delay performance close to IEEE 802.1CM frame preemption algorithm.

Highly resolved and accurate air pollution maps are valuable resources for many issues related to air quality including exposure modeling and urban planning. Due to the high equipment costs, there are limited high quality monitoring stations (HQMS) in cities. In order to achieve high resolution air pollution maps, a large number of mobile sensors are required. Besides, mobile sensors require frequent calibrations with the HQMS to maintain data accuracy. Existing work on route design for mobile sensors largely focuses on data reconstruction, which either ignores calibration or views it as an independent problem. To improve the accuracy of data reconstruction, this paper proposes a novel scheme that jointly considers sensor calibration and data reconstruction in route design for mobile sensors. We formulate a novel sensor route planning problem (SRPP) which aims to maximize the mutual information and guarantee the accuracy of measurements through sensor calibration. A heuristic algorithm is proposed to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. Simulation results show that, compared with traditional approach, our approach can reduce 83% root mean square error (RMSE) on average.

It's a great challenge to offer a fine-grained and accurate air quality monitoring service in urban areas limited to the cost of the professional facilities. With the development of the wireless sensor networks (WSNs), it brings new opportunity to achieve this goal at low cost. However, WSNs are quite different on temporal and spatial distribution, some WSNs even have irregular real-time features, which makes it a hard problem to use the data collected from different WSNs to achieve the same goal. In this paper, we propose a framework for air quality estimation based on multi-source heterogeneous data collected from WSNs. We collect five kinds of data from different sources in real world, including the data with irregular real-time features. We divide the data sets into three sub classifiers to make the analysis and get the final results with an extreme learning machine (ELM) based multilayer perceptron model. The results show that our method outperforms other methods, and the precision of classification can be 90.8%.

Air pollution seriously affect people's lives, among which PM2.5is especially harmful for humans health. Although many countries have established fixed air quality monitoring stations (AQMS) to monitor air pollution, the costs of constructing and maintaining for AQMS are extremely expensive and the density of AQMS is very low. To acquire fine-grained concentration of PM2.5, this paper have proposed a novel Bayesian based kernel method. Our model leverage heterogeneous data which jointly using images information, camera lens information, GPS information and magnetic sensor information. To study the relationship between PM2.5concentration and images information, we have established a crowdsourcing system and have collected photos for consecutive 16 months. The performance of the proposed method has been evaluated thoroughly by real dataset we have collected. The results show that, compared with three baselines, our proposed algorithm can reduce up to 35% prediction error in average.

Mobile applications and Internet of Things applications increasingly require one mobile device to exchange data with another collocated device. Despite the introduction of multiple standard device-to-device communication protocols (i.e., WiFi direct, Bluetooth, Bluetooth Low Energy, NFC), transferring data across devices remains hard for mobile programmers for three reasons. 1) different devices support different D2D communication interfaces, and therefore the available D2D communication channels are dynamically decided by the peers; 2) different D2D interfaces have different features (connection establish time, data rate, energy consumption) and different performances under different contexts, complicating the decision which channel to use; 3) implementing and debugging data transmission functionalities requires knowing the low-level details of communication protocols, which is difficult and error prone. To solve the above mentioned problem, this paper presents a runtime framework for context-sensitive device-to-device communication. The presented framework consists of two major components: 1) a set of encapsulated D2D data transmission interfaces to reduce the programming efforts; 2) a context-sensitive communication channel selection algorithm to select the optimal communication channel as defined by the dynamic context. We design and implement the runtime framework, and evaluate its performance in terms of the required programming effort, energy consumption and data transmission latency under different contexts.

Mobile Crowd sensing is a new paradigm that encourages ordinary people to collect and share sensing data with their smart devices. However, the uncontrollable data quality is one of the critical problems that is potentially harmful to guarantee the availability and preciseness of mobile crowd sensing based services. In this paper, we propose a quality aware data collection mechanism based on a realistic scenario where participants arrive and report their conditions sequentially one by one. When a participant arrives, the mechanism first forecasts the amount of high quality data he#x002F;she may contribute by employing the expectation of Binomial-Poisson distribution, and then a two-level iterative algorithm is employed to calculate its parametric values. After, our designed mechanism decides to select the participant or not by combining with his#x002F;her requested reward. Extensive simulation results well justify the effectiveness and robustness of our approach, compared with another schemes.

PM2.5 (particulate matter in the atmosphere with a diameter no more than 2.5 microns) in the air can cause great damage to human beings. It's a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as the required facilities are very expensive and huge. Since the PM2.5 has a significant scattering effect on visible light, the large-scale user-contributed image data collected by the mobile crowd sensing bring a new opportunity for understanding the urban PM2.5. After the analysis of image data, we find that several image features are very discriminative in PM2.5 inference. In this paper, we propose a fine-grained PM2.5 estimation method based on the random forest model without any PM2.5 measurement devices. We evaluate our approach with experiments based on five data sources: the meteorological data, the traffic data, the records from the monitoring sites, the POIs and the photos collected. The experimental results show that we have a high evaluation accuracy on PM2.5 estimation (precision: 0.875, recall: 0.872), which outperforms existing methods (Logistic, Naive Bayes, Random Tree, and BP ANN).

Location based services are getting increasingly popular in participatory sensing systems. They make use of location information on the mobile devices to support applications that improve personal health, object search, and entertainment. However, GPS positioning consumes a lot of energy, which can drain a mobile device's battery. Although WiFi localization and cell tower localization have been suggested as alternatives, they have lower localization accuracy and limited coverage. In this paper, we suggest a novel solution for multiple mobile devices to perform collaborative localization to reduce energy consumption and provide accurate localization. We divide the mobile devices into two groups, the aggregator group and the collector group. The aggregator group turns on their GPS periodically, while the collector group uses the locations of the aggregators to estimate their own locations. We formulate the aggregator set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces showed that our proposed solution can save up to 88% of the energy of the entire network.

Web users are immersed in their roles as information producers and propagation pushers. They are unaware of being potential threats to privacy-protection towards themselves and their friends. It is necessary to know who they should beware of most in their friend-networks once their privacy information is divulged inadvertently. In this paper, we aim to identify the vulnerable friend who maximizes the dissemination of privacy information. First we develop a Privacy Receiving-Disseminating (PRD) model to simulate the iterative course of privacy information dissemination within social graph. The subgraph constituted of those users who are involved in the dissemination, called Ultimate Circle of Disseminating (UCD), is then detected by an iterative algorithm. The contribution of each direct friend could be evaluated by comparing the disseminating intensities of detected UCDs before and after unfriending himself. The performance of our work has been validated empirically with the comparison of different unfriending strategies.

The increasingly popular smartphones enable participatory sensing systems to collect location-based sensing data for different tasks. However, GPS positioning is very energy consuming, which could drain a mobile device's battery quickly. High energy consumption may threaten the participants and reduce the sustainability of the participatory sensing systems. In this paper, we propose a collaborative localization strategy with load balancing. Simulations with real traces showed that our proposed solution can save more than 80% of the energy consumption for localization in the entire network with load balancing.

How to organize and visualize big amount of text messages stored on one's mobile phone is a challenging problem, since they can hardly be organized by threads as we do for emails due to lack of necessary metadata such as "subject" and "reply-to". In this paper, we propose an innovative approach based on clustering algorithms and natural language processing methods. We first cluster the text messages into candidate conversations based on their temporal attributes, and then do further analysis using a semantic model based on Latent Dirichlet Allocation (LDA). Considering that the text messages are usually short and sparse, we trained the model using a large scale external data collected from twitter-like web sites, and applied the model to text messages. In the end, the text messages are organized as conversations based on their topics. We evaluated our approach based on 122,359 text messages collected from 50 university students during 6 months.