Foundational vision-language models (VLMs), such as CLIP, are emerging as a promising paradigm in vision tasks due to their strong generalization ability. Nevertheless, adapting them to downstream tasks remains challenging, especially in biomedical imaging, where scarce annotations, low-contrast features and complex patterns hinder model adaptation. Thus, prompt tuning is employed to facilitate the adaptation of VLMs. However, current prompt tuning methods like Context Optimization (CoOp) mainly learn a single yet static prompt which is applied to all images, and such one-size-fits-all prompt cannot describe the case-specific diagnostic cues in biomedical data, compromising the adaptation of VLMs. To this end, we propose a Dynamic Prompt Policy learning method that enables efficient adaptation of Biomedical VLMs (BioDPP) for accurate and highly generalizable few-shot biomedical image classification. Specifically, we conceptualize the learnable context as an agent, and present a paradigm of learning a dynamic prompting policy, rather than obtaining a single yet static prompt. Wherein, a dual-reward mechanism is developed to guide policy learning via the feedback on both classification decision and the consistency between the prompt and the context, steering the agent to generate context-aware prompts. Moreover, we devise adaptive baseline stabilization to dynamically regulate reward advantage value throughout the training process, enabling policy refinement in a complex reward space tailored to biomedical VLMs. Extensive experiments are conducted on 10 biomedical datasets, and the results reveal that our BioDPP achieves superior performance, demonstrating more efficient prompt optimization in biomedical VLMs.

Federated Adversarial Learning (FAL) is a robust framework for resisting adversarial attacks on federated learning. Although some FAL studies have developed efficient algorithms, they primarily focus on convergence performance and overlook generalization. Generalization is crucial for evaluating algorithm performance on unseen data. However, generalization analysis is more challenging due to non-smooth adversarial loss functions. A common approach to addressing this issue is to leverage smoothness approximation. In this paper, we develop algorithm stability measures to evaluate the generalization performance of two popular FAL algorithms: Vanilla FAL (VFAL) and Slack FAL (SFAL), using three different smooth approximation methods: 1) Surrogate Smoothness Approximation (SSA), (2) Randomized Smoothness Approximation (RSA), and (3) Over-Parameterized Smoothness Approximation (OPSA). Based on our in-depth analysis, we answer how to properly set the smoothness approximation method to mitigate generalization error in FAL. Moreover, we identify RSA as the most effective generalization error reduction method. In highly data-heterogeneous scenarios, we also recommend employing SFAL to mitigate the deterioration of generalization performance caused by heterogeneity. Based on our theoretical results, we provide insights to help develop more efficient FAL algorithms, such as designing new metrics and dynamic aggregation rules to mitigate heterogeneity.

Myocardial infarction remains a leading cause of cardiovascular mortality, making its early detection and localization via electrocardiograms (ECGs) critical for timely intervention. Despite advances in deep learning-based ECG analysis, two key challenges persist: (1) Severe class imbalance biases models toward dominant categories, hindering the learning of discriminative patterns for underrepresented subtypes (e.g., posterior, extensive anterior infarctions). (2) The inability to adequately model complex spatiotemporal dependencies in ECGs limits the effectiveness of current approaches. To address these challenges, we propose SPG-Net, a novel structure-aware spatiotemporal graph network that integrates clinical priors with dynamic spatiotemporal dependencies of ECG for MI localization. The framework extracts high-resolution lead-wise features and constructs anatomically grounded graphs using class-specific lead contribution maps. A dedicated graph attention module integrates static anatomical priors, inter-lead spatial interactions, and temporal dependencies to generate ECG feature representations that preserve physiological consistency while accommodating individual variability. Furthermore, we design a decoupled classification mechanism to reduce interference from overlapping labels, significantly improving minority-class recognition. Comprehensive experiments on PTB and PTB-XL datasets demonstrate SPG-Net's superior overall performance and significantly enhanced minority-class recognition, validating its efficacy in class-imbalanced MI localization.

Electrocardiogram (ECG) classification plays a vital role in the early diagnosis of cardiovascular diseases. Multi-label ECG classification remains challenging despite advances in deep learning. This is mainly due to two factors: (1) the complex spectral characteristics of ECG signals, and (2) the limited ability of conventional models to capture nonlinear physiological patterns. In this work, we propose PSE- KAN et, a novel frequency-aware and spline-adaptive framework tailored for multi-label ECG classification. Central to our design is the Power Spectrum Enhancement (PSE) module, which mitigates the spectral bias commonly observed in deep neural networks, the tendency to overfit low-frequency components through a unified frequency-domain processing strategy. This strategy comprises three coordinated components: spectral reweighting balances across frequency bands, low-frequency anchoring preserves diag-nostic waveform structures, and Gaussian-based band emphasis selectively enhances underrepresented high-frequency features. Together, they enable task aware, frequency selective enhance-ment that enriches class relevant spectral representations. To further improve the modeling of complex label dependencies, we design KANMixLinear (KML), a classification head that integrates linear projection with learnable B-spline interpolation via a data-dependent gating mechanism. This design provides flexible and expressive decision boundaries suited for multi-label scenarios. Experimental results show that PSE-KANet performs better in multi-label ECG classification tasks, outperforming existing state-of-the-art methods across metrics, including AUC, Fl-score, and Recall. The code and data are publicly available at: https://github.com/yang-161224IBIBM.

Automatic summarization of discharge summaries aims to generate a concise yet informative overview from complex electronic health records (EHRs), thus alleviating the documentation burden on clinicians. Existing methods usually employ pre-trained Transformer architectures to capture textual dependency via self-attention mechanism. However, these methods fail to utilize the information of multi-level hierarchical structure in EHR, compressing the understanding of intricate semantic interaction among different structure levels. In this work, we propose HiGraphSum, a hierarchical hypergraphbased dual-stage model for automatic summarization of discharge summaries. In the first stage, HiGraphSum constructs a hierarchical hypergraph that integrates heterogeneous wordsentence graphs and sentence-based hypergraphs to capture hierarchical and contextual information, facilitating global document structure and intricate clinical semantics learning. In the second stage, we introduce a probability concentration strategy (PCS) to refine summary generation by sharpening the output distribution, thereby enhancing the factual consistency of the generated summaries. Extensive experiments on discharge summarise datasets demonstrate that HiGraphSum outperforms state-of-the-art baselines.

Myocardial infarction (MI) is one of the most dangerous cardiovascular diseases, typically diagnosed using electrocardiogram (ECG). While many deep learning methods exist for MI detection, they often overlook the correlations and medical knowledge between leads of ECG, resulting in limited interpretability. To address the challenges, we propose a knowledge-guided multi branch dense graph neural network (KGD-GNN) for MI localization. The proposed method incorporates clinical diagnostic knowledge to represent ECG as a graph, capturing inter-lead relationships. A multi-branch graph convolutional module is then used to extract disease-specific features through dense graph convolution. Evaluations on the public PTB-XL dataset demonstrate the superior performance of KGD-GNN across multiple metrics.

The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in assisting physicians with MI localization. Traditional MI localization methods are purely data-driven, and the quality of the data significantly affects the model's performance, particularly in the localization of rare MI. We propose a knowledge-driven graph representation learning (KD-GRL) framework which is designed to guide deep learning models in identifying key features for MI localization using prior knowledge. The MI localization knowledge graph (KG) is constructed by integrating medical knowledge about MI localization, including ECG leads and morphological manifestations, the correlations between MI localization labels, diagnostic rules, and patient demographic information. KG effectively represents the relationships among various entities, which include ECG signal entities, morphological feature entities, and demographic feature entities. The embeddings of these entities are obtained using parallel patient multi-feature extractors. Additionally, a KG aggregation method based on edge relation projection (ERP) is proposed to aggregate the relational information in the MI localization KG. Ultimately, the MI localization task is transformed into a link prediction task between patient entity and localization label entities within the KG. We conduct experiments on two public datasets, PTB and PTBXL, achieving F1-scores of 48.90% and 46.06%, respectively, both surpassing the comparison methods. Additionally, due to the incorporation of diagnostic knowledge, our method outperforms the comparison methods in localizing rare MIs.

Effective diabetes management requires regular and accurate blood glucose monitoring; however, traditional invasive methods often cause discomfort and inconvenience. Non-invasive techniques such as photoplethysmography (PPG) have been explored, though single-wavelength PPG systems are limited by the overlapping absorption characteristics between glucose and other biological components, such as water and fat. In this study, a novel multi-wavelength PPG system integrated with temperature and humidity sensors is introduced, coupled with a neural network framework featuring attention mechanisms to enhance glucose prediction. The system employs six optical sensors covering wavelengths from the visible to near-infrared (NIR) spectrum, enabling deeper tissue penetration and enhanced glucose specificity by targeting distinct absorption peaks-especially those above 1000 nm. The system was validated using a robust dataset of 26,063 measurements from 254 participants. The experimental results demonstrate significant improvements, with the model achieving 86.49% compliance with the ISO 15197: 2013 standards and 91.80% of measurements falling within Zone A of the Parkes error grid. The introduction of multiple wavelengths clearly improves performance over single-wavelength systems, and wavelengths above 1000 nm were shown to have a higher contribution in glucose prediction. In addition, the incorporation of temperature and humidity data also enhanced performance by accounting for environmental and physiological factors, and that demographic and meal-related factors significantly impact prediction accuracy, thereby underscoring the potential of this system as a reliable, non-invasive, and personalized glucose monitoring tool.

It is crucial to predict the in-hospital mortality for improving clinical decision-making and optimizing hospital resource allocation. Many recent studies have attempted to integrate multimodal data, such as digital time series and clinical notes from electronic health records (EHRs), to improve the performance of mortality prediction. Although current methods show good performance, it is often difficult to effectively capture fine-grained inter-modal correspondences. In addition, the different distributions and heterogeneous properties of the various modalities lead to modality gaps that severely affect the effectiveness of modal fusion. To overcome these limitations, we propose an Attention-based Multimodal Fusion with Adversarial network (AMFA) for in-hospital mortality prediction. In AMFA, feature extraction is first performed to obtain modality-specific features, and then an attention mechanism is employed to capture inter-modal interrelationships. The main difference with existing methods is that AMFA achieves this by calculating the relative importance of individual features focusing attention on the most relevant features and eliminating irrelevant features, and then reassigning attention between relevant features to obtain finer semantic relevance. Subsequently, we introduce a discriminator network that addresses the modality gap by adjusting the distribution of various modal representations through adversarial training. We evaluate AMFA on two publicly available datasets, and experimental results show that AMFA outperforms several state-of-the-art models in the task of in-hospital mortality prediction.

Myocardial infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). 12-lead electrocardiogram (ECG) is regarded as an effective noninvasive method for localization of MI. Considering the clinical reality that sometimes patients with heart disease often have multiple MI, and that real-world data faces an imbalance problem for each class of MI. In this study, we propose a data augmentation aided multichannel attention fusion network for multi-label MI localization (DAMAL), fusing both lead correlation and label correlation in a framework to localize MI. Specifically, DAMAL constructs separate channels for each of 12-lead ECG, and builds a matrix channel attention (MCA), to enhance the representativeness of the input features. A spatial & channel fusion module is proposed which includes a channel attention (CA) module to capture the attention levels of different leads, and a spatial attention (SA) module to capture the spatial relationships among the leads. Additionally, data augmentation is performed for multi-label datasets with minority classes to capture the optimal classification thresholds for each class of labels and improve the model performance. Furthermore, DAMAL exhibits good interpretability by the Grad-CAM-based visualization. Experimental results show that DAMAL performs better than other state-of-the-art methods in classifying multi-label MI across various metrics, including F1 score, AUROC, and Hamming Loss.

Predicting future health status based on historical patient visits is one of the essential tasks in healthcare. Many existing approaches attempt to enhance the representation learning capability of models by incorporating relevant medical knowledge, but their effectiveness is severely affected by the incompleteness and noise of the knowledge graphs. Moreover, due to the inability to capture temporal features at a fine-grained level, most existing methods also have limitations in learning the temporal development of patients’ health status. To address these issues, we propose a Knowledge-Enhanced Difference-Aware clinical time series representation learning model (KEDA) for diagnosis prediction. In this model, we first combine the medical ontology graph and co-occurrence graph, and use hierarchical graph convolution and contrastive learning methods to enhance the semantic representation of medical entities. After that, a task-specific difference-aware temporal module is designed to improve the accuracy of patient representation, which adds two novel gated units in the original GRU to fuse multi-type clinical information based on the relationship between different types of data and prediction tasks and capture fine-grained temporal evolution of patient health status. We validate our model on two publicly available datasets, and the experimental results demonstrate that KEDA outperforms the state-of-the-art methods.

Triplet extraction is one of the fundamental tasks in biomedical text mining. Compared with traditional pipeline approaches, joint methods can alleviate the error propagation problem from entity recognition to relation classification. However, existing methods face challenges in detecting overlapping entities and overlapping relations, which are ubiquitous in biomedical texts. In this work, we propose a novel pipeline method of end-to-end biomedical triplet extraction. In particular, a span-based detection strategy is used to detect the overlapping triplets by enumerating possible candidate spans and entity pairs. The strategy is further used to capture different contextualized representations via an entity model and a relation model, respectively. Furthermore, to enhance interrelation between spans, entity information from the output of the entity model is used to construct the input for the relation model without utilizing any external knowledge. Our approach is evaluated on the drug-drug interaction (DDI) and chemical-protein interaction (CHEMPROT) datasets, exhibiting improvement of the absolute F1-score in relation extraction by 3.5%-3.7% compared prior work. The experimental results highlight the importance of overlapping triplet detection using the span-based approach, acquisition of various contextualized representations via different in-domain pre-trained language models, and early fusion of entity information in the relation model.

Arrhythmia is an abnormal heart rhythm, a common clinical problem in cardiology. Long-term or severe arrhythmia may lead to stroke and sudden cardiac death. The electrocardiogram (ECG) is the most commonly used tool to diagnose arrhythmia. However, the traditional diagnosis relies on experts for manual interpretation, which is time-consuming and laborious. In recent years, many automatic arrhythmia detection methods have emerged due to advancements in deep learning. These methods can reduce manual intervention and improve diagnostic efficiency. However, extracting useful features from raw ECG signals for arrhythmia detection is still challenging due to the low frequency of ECG signals and noise distribution. In this paper, we propose a novel hidden attention residual network (HA-ResNet) for automated arrhythmia classification. In this model, the one-dimensional ECG signals are first converted into two-dimensional images and fed into an embedding layer to obtain the relevant shallow features in ECG. Then, a hidden attention layer combining Squeeze-and-Excitation (SE) block and Bidirectional Convolutional LSTM (BConvLSTM) is used to further capture the deep Spatio-temporal features. We evaluate our HA-ResNet on two public datasets and achieve F1 scores of 96.0%, 96.7%, and 87.6% on 2s segments, 5s segments, and 10s segments, respectively, which significantly outperform the existing state-of-the-art approaches. The experimental results demonstrate the effectiveness and generalization of our method.

Disease prediction based on the Electronic Health Record (EHR) is an important task in healthcare. EHR records patients’ every visit by time, and there are many kinds of medical codes within a visit, therefore, EHR has characteristics of temporal irregularity and hierarchical structure. Some recent works employ BERT-style models to process EHR data for disease prediction. However, few of these models can give consideration to capture both the interaction between medical codes and the impact of temporal irregularity. To solve this problem, we propose the Double-Level BERT-style model (DL-BERT). Considering EHR’s hierarchical structure, the model contains a code-level and a visit-level representation layer which can learn the relationship between medical codes and temporal influence respectively. In the code-level representation layer, the model achieves the representation power by employing external medical ontologies to provide multi-resolution information of medical codes and the Transformer to embed medical codes. Besides, the model adopts two pre-training tasks to enhance the ability to capture the link between different kinds of codes. In the visit-level representation layer, DL-BERT utilizes a special time-aware Transformer to model temporal information. And the model adopts a visit-level pre-training task for better learning context information. Experiments are conducted on two real-world healthcare datasets and show that our model outperforms all baselines demonstrating the effectiveness of our model.

Early detection of paroxysmal atrial fibrillation (AF) is valuable in determining treatment options and diagnosing complications, and identifying the number and duration of AF episodes in the dynamic electrocardiogram (ECG) may help advance the study of pathological AF mechanisms. Current computer-aided ECG signal diagnosis methods can be divided into two categories: (a) methods based on every single heartbeat split from ECG records, which cannot use information from adjacent heartbeats, and (b) methods based on a segment of ECG records that contain multiple heartbeats, which can only give a classification of the whole segment but cannot identify the categories of each heartbeat in the record at a fine granularity. Few methods can exploit the relationship between heartbeats as well as identify the onset and end of disease episodes. To fill this gap, we designed an improved U-Net model named Percept U-Net to detect AF episodes from the ECG records. In this model, we propose the percept attention to replace skip connections in U-Net to integrate low-level features with high-level features, thereby enhancing the ability to detect and discriminate between normal and abnormal heartbeats. Experimental results show that Percept U-Net achieves higher classification and localization accuracy with less computational overhead and paraments than other comparative methods.

Nuclei segmentation is valuable in histopathological image analysis, but labeling nuclei is costly. Different organs, pa-tients and diseases will lead to high variability in the morphology of nuclei, the structure of tissues, etc., which is difficult to elim-inate. Inconsistent staining operations and scanning operations will cause variability in histopathological image style. Relying on a small amount of labeled data, it is hard for the model to adapt to the high variability among histopathological images. Therefore, it is necessary to exploit the value in the massive unlabeled data. However, because the existing pretext tasks in self-supervised learning do not well consider the characteristics of histopathological images and segmentation task, the same for the existing data augmentation approaches in contrastive learning, they are not suitable for nuclei segmentation. In this paper, the proposed method, named NormToRaw, takes into consideration the characteristics of nuclei segmentation, which can learn semantic information from different stains by style transfer. A generative adversarial network is used to transfer the normalized image to the raw image. Pre-trained on more than 8,000 unlabeled images and trained on 16 labeled images, the experimental results of 5 pre-trained models showed that the proposed method is effective for improving the performance of nuclei segmentation.

Predicting the future risk of cardiovascular diseases from the historical Electronic Health Records (EHRs) is a significant research task in personalized healthcare fields. In recent years, many deep neural network-based methods have emerged, which model patient disease progression by capturing the temporal patterns in sequential visit data. However, most existing methods that integrate different types of clinical data often do not fully consider the impact of the patient's age and the irregular time interval between consecutive medical records on the patient's disease development. To address these challenges, we propose a Time-Aware Multi-type Data fUsion Representation learning framework (TAMDUR) for cardiovascular diseases (CVDs) risk prediction. In this framework, we design a time-aware decay function, which is based on the patient's age and the elapsed time between visits, to model the disease progression pattern. Then, a parallel combination of bidirectional long short-term memory (Bi_LSTM) network and convolutional neural network (CNN) is constructed to respectively learn the temporal and non-temporal features from various types of clinical data. Finally, a multi-type data fusion representation layer based on self-attention is utilized to integrate various features and their correlations to obtain the final patient representation. We evaluate our model on a real medical dataset, and the experimental results demonstrate that TAMDUR outperforms the state-of-the-art approaches.

Predicting the future diagnoses from patients’ historical Electronic Health Records (EHR) is a significant task in healthcare. EHR consist of multiple modal data, each modality has different features and contains a wealth of information of patients. However, most of the existing EHR-based prediction methods either only use unimodal data, or fail to fully explore the correlation between different modalities when fusing multimodal data. To address these challenges, we propose a Multimodal Attention-based fusIon Networks (MAIN) for diagnosis prediction. In this model, we first design different feature extraction modules for each modality. Then, an inter-modal correlation module which contains two layers is applied to capture the intermodal correlation. Finally, a multimodal fusion module based on weighted averaging is utilized to integrate the representations derived from different modalities and their correlation to obtain the patient representation for diagnosis prediction. We evaluate our proposed model on two medical datasets, and the experimental results demonstrate the effectiveness of MAIN.

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automated models based on machine learning have been proposed to predict the onset of cardiovascular disease with the premorbid information of patients extracted from their historical Electronic Health Records (EHRs). However, it is a tough job to select proper features from longitudinal and heterogeneous EHRs, and also a great challenge to obtain accurate and robust representations for patients. In this paper, we propose an entirely end-to-end model called DeepRisk based on attention mechanism and deep neural networks, which can not only learn high-quality features automatically from EHRs, but also efficiently integrate heterogeneous and time-ordered medical data, and finally predict patients' risk of cardiovascular diseases. Experiments are carried out on a real medical dataset and results show that DeepRisk can significantly improve the high-risk prediction accuracy for cardiovascular disease compared with state-of-the-art approaches.

Based on the traffic accident data of Beijing China in 2012, we combined with a variety of municipal administration data, used the geographically weighted regression (GWR) method to study spatial non-stationarity and heterogeneity of the traffic accidents, and analyzed the causes of space regional in traffic accident black spots. Experimental results demonstrated that: (1) The GWR model detects change of the coefficient and explains the significance of coefficient change as well. It is suitable for interpreting the distribution regularity and the causes of traffic accident black spots at the microscopic level. (2) By analyzing the importance of the factors on traffic accident black spots, we note the following order of factors according to the significance: weather, hospital, park, subway station, curvature of the roads and population density. The influence of each factor on traffic accident black spots differs in spatial heterogeneity characteristics.

Database driven IP geolocation is a convenient and common way to determine geographic location of an IP address. However, the underlying problem is that it is often difficult for users to determine which provider is reliable enough to meet their own scenarios. In this paper, we tackle this challenge in a data fusion perspective. We first evaluate the consistency degree of data entries among 5 free geolocation databases and employ it as an indicator of data quality assessment. We find that this indicator varies by geographic scope and granularity for a certain provider. Therefore we are able to evaluate data quality for different parts and dimensions within a database. Then a data fusion method utilizing data consistency degree and quota-based votes is proposed and analyzed. Over 40 million IP geolocation ground truth data in China, i.e., more than 10% of the total address space allocated to China, is applied to verify the effectiveness and advantage of the proposed method. In this work, we provide insights into comprehensive utilization of multi-databases characteristics for data entry fusion in the absence of enough priori knowledge.

Summary Message replication is often used to improve the delivery ratio in delay‐tolerant networks because of the short‐lived wireless connectivity environment. However, packet replication may easily incur large resource consumption and finally result in network congestion. This paper proposes a probabilistic packet acceptance and drop algorithm (PAD), which adaptively controls congestion for delay‐tolerant networks. In PAD algorithm, the queue length and the input/output rate are combined to detect congestion. Based on the congestion state, each node determines the probability of accepting or dropping packets to obtain a good trade‐off between high delivery ratio and low overhead. Furthermore, based on the birth–death model, we construct the continuous‐time Markov chain to analyze the delivery ratio of a packet. Theory analysis and simulation results show that PAD increases the delivery ratio by more than 130% with least overhead. Meanwhile, it also achieves the shortest average end‐to‐end delay when the buffer of a node is severely limited. Copyright © 2015 John Wiley & Sons, Ltd.

Due to the intermittent connectivity of content-centric opportunistic networks, data dissemination is often performed by replication which easily leads to network congestion. Many existing message forwarding or dropping strategies provide congestion avoidance based on only the node-related metrics or the message-related metrics, i.e. the meeting history between nodes, the number of subscribers, message hop count etc. However the temporal validity of user interest is ignored in most cases. In fact, the interests of users may have different durations. A message is unnecessary to be forwarded when the nodes have lost interests in it. Therefore, we present the first attempt to apply the validity duration of user interest in message dropping decision and present a novel temporal validity based buffer management scheme. In this scheme, a multi-attribute utility is designed to decide the dropping priority of messages from the standpoints of node, message and interest. Simulation results prove the superiority of our scheme with respect to delivery ratio, dissemination speed and overhead.

End-to-end reliability in Delay Tolerant Networks (DTNs) is challenging and complicated because of high delay and absence of stable end-to-end path in the intermittently-connected mobile environment. Some existing acknowledgement mechanisms use active forwarding to provide the end-to-end reliability, while incurring excessive retransmissions or replications. The other passive mechanisms aim to reduce the storage overhead but may suffer a large delay. To improve the storage-delay tradeoff, we propose a Congestion Level based end-to-end ACKnowledgement (CL-ACK) mechanism, which adaptively adjusts the spread manner of ACK packets according to the ratio of drops over replications. Simulation results show that CL-ACK effectively controls resources consumption, reduces end-to-end delay, and achieves high message delivery rate.