Out-of-Town (OOT) recommendation aims to provide personalized suggestions for users in unfamiliar cities. However, OOT recommendation faces two fundamental challenges: the difficulty of reasoning across modalities, as preference signals in disparate formats such as images and text are hard to compare; and the preference deviation problem, since a user's resident and tourist preferences often diverge, rendering simple preference transfer ineffective. To address these challenges, we propose Distinguishing Resident and Tourist Preferences via Multi-Modal LLM Alignment for Out-of-Town Cross-Domain Recommendation (DiMA), a framework for re-ranking Points of Interest (POIs). To tackle the multimodal challenge, DiMA first leverages Multimodal Large Language Models and Large Language Models (LLMs) to transform heterogeneous POI data into unified semantic tags, enabling both cross-modal reasoning and efficient downstream processing. To address preference deviation, a ``teacher'' LLM executes a custom Chain-of-Thought (CoT) process to disentangle resident and tourist preferences from multi-city histories for re-ranking. Finally, a lightweight student model learns this CoT reasoning via Supervised Fine-Tuning and is then refined with Direct Preference Optimization to align with true user choices, with the potential to surpass the teacher. Extensive experiments on a real-world dataset demonstrate that DiMA significantly enhances the performance of baseline models in the OOT recommendation re-ranking task.

Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.

In modern medicine, the widespread use of medical imaging has greatly improved diagnostic and treatment efficiency. However, these images contain sensitive personal information, and any leakage could seriously compromise patient privacy, leading to ethical and legal issues. Federated learning (FL), an emerging privacy-preserving technique, transmits gradients rather than raw data for model training. Yet, recent studies reveal that gradient inversion attacks can exploit this information to reconstruct private data, posing a significant threat to FL. Current attacks remain limited in image resolution, similarity, and batch processing, and thus do not yet pose a significant risk to FL. To address this, we propose a novel gradient inversion attack based on sparsified gradient matching and segmentation reorganization (SR) to reconstruct high-resolution, high-similarity medical images in batch mode. Specifically, an $L_{1}$ loss function optimises the gradient sparsification process, while the SR strategy enhances image resolution. An adaptive learning rate adjustment mechanism is also employed to improve optimisation stability and avoid local optima. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in both visual quality and quantitative metrics, achieving up to a 146% improvement in similarity.

Next Point-of-interest (POI) recommendation has been widely used in real scenarios to predict the next possible location based on user behavior patterns. However, existing methods predominantly rely on spatio-temporal associations and check-in sequence relationships between users and POIs, which fall short for users with limited interactions with POIs. Moreover, user preferences are inherently multi-dimensional, rendering user selections often influenced by multiple factors such as location categories and multi-modal information. To mitigate these issues, we introduce aMulti-Modal Knowledge GraphModeling ofMulti-Dimensional User Preferences for Next-POIRecommendation (M4Recfor short). First, we define a multi-modal knowledge graph to organize the relationships among users, locations, categories, and multi-modal information. Subsequently, we use the multi-modal knowledge graph-based relation-aware network to derive comprehensive entity representations from the constructed knowledge graph. Next, employing the temporal knowledge prediction method, we predict the user's next-POI category and next-POI. Finally, the final recommendation results are obtained by enhancing the corresponding location prediction scores through category semantics. Extensive experimentation conducted on real-world datasets validates the superiority of our proposed method over state-of-the-art competitors.

Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph (KG) information into dialogue generation and recommendation remains a pressing issue. Traditional approaches typically combine KG information directly with dialogue content, which often struggles with complex semantic relationships, resulting in recommendations that may not align with user expectations.

Personalized recommendation systems play a critical role in helping users discover relevant content amidst information overload. This paper proposes DisenRec, a novel sequential recommendation framework that addresses key limitations in existing approaches. By constructing a heterogeneous graph that incorporates multidimensional contextual information, we first learn initial user/item representations using a Heterogeneous Graph Attention Network. We then disentangle user preferences into dynamic interest preferences (modeling temporal behavioral patterns) and static attribute preferences (capturing stable trait-based inclinations) through causal decomposition and orthogonal constraints. A context-aware fusion module dynamically balances these components during prediction. Experiments on Amazon-Books and MovieLens-1M datasets demonstrate that DisenRec significantly outperforms state-of-the-art baselines in HR@10 and NDCG@10 metrics. Our model reduces representation entanglement, enhances preference modeling granularity, and improves both recommendation accuracy and interpretability by uncovering the causal mechanisms driving user decisions.

Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' information from the auxiliary domain to the target domain. However, these systems face two main issues: the underutilization of multimodal data, which hinders effective cross-domain alignment, and the neglect of side users who interact solely within the target domain, leading to inadequate learning of the target domain's vector space distribution. To address these issues, we propose a model leveraging Multimodal data and Side users for diffusion Cross-domain recommendation (MuSiC). We first employ a multimodal large language model to extract item multimodal features and leverage a large language model to uncover user features. Secondly, we propose the cross-domain diffusion module to learn the generation of feature vectors in the target domain. This approach involves learning feature distribution from side users and understanding the patterns in cross-domain transformation through overlapping users. Subsequently, the trained diffusion module is used to generate feature vectors for cold-start users in the target domain, enabling the completion of cross-domain recommendation tasks. Finally, our experimental evaluation of the Amazon dataset confirms that MuSiC achieves state-of-the-art performance, significantly outperforming all selected baselines. Our code is available: https://github.com/zhangf16/MuSiC.

Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights. Some methods try to include inter-session data but struggle with noise and irrelevant information, reducing performance. Additionally, most models rely on item ID co-occurrence and overlook rich semantic details, limiting their ability to capture fine-grained item features. To address these challenges, we propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP. First, we introduce a pluggable embedding module based on large language models (LLMs) to generate high-quality semantic representations, enhancing item embeddings. Second, HIPHOP utilizes graph neural networks (GNNs) to model item transition relationships and incorporates a dynamic multi-intent capturing module to address users' diverse interests within a session. Additionally, we design a hierarchical inter-session similarity learning module, guided by user intent, to capture global and local session relationships, effectively exploring users' long-term and short-term interests. To mitigate noise, an intent-guided denoising strategy is applied during inter-session learning. Finally, we enhance the model's discriminative capability by using contrastive learning to optimize session representations. Experiments on multiple datasets show that HIPHOP significantly outperforms existing methods, demonstrating its effectiveness in improving recommendation quality. Our code is available: https://github.com/hjx159/HIPHOP.

The importance of group communication in the context of the Internet of Things (IoT) is growing, yet the security and stability of this communication are facing significant challenges. The prevailing distributed group key management (GKM) schemes are ill-suited to resource-constrained devices. Furthermore, those that rely on servers are vulnerable to single-point failures and Byzantine risks. The distributed, immutable, and automatic execution of smart contracts on blockchains may offer a potential solution to these problems. This article puts forth a multilayer blockchain-based IoT GKM scheme with Byzantine fault tolerance (BFT). The scheme oversees the management of IoT device subgroups through the deployment of blockchain and smart contracts on edge servers while overseeing the entire device group in a hierarchical structure. A redundant selection mechanism based on hash mapping has been designed to guarantee reliable communication between disparate blockchains and devices. Concurrently, the scheme incorporates a server detection mechanism for Byzantine behavior, thereby ensuring the stability of the blockchain. The results of the experimental analysis demonstrate that the scheme exhibits enhanced security and fault tolerance.

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms.

Model Inversion (MI) attacks based on Generative Adversarial Networks (GAN) aim to recover private training data from complex deep learning models by searching codes in the latent space. However, this method merely searches in a deterministic latent space, resulting in suboptimal latent codes. Additionally, existing distributional MI schemes assume that an attacker can access the structures and parameters of the target model, which is not always feasible in practice. To address these limitations, this paper proposes a novel Distributional Black-Box Model Inversion (DBB-MI) attack by constructing a probabilistic latent space for searching private data. Specifically, DBB-MI does not require the target model’s parameters or specialized GAN training. Instead, it identifies the latent probability distribution by integrating the output of the target model with multi-agent reinforcement learning techniques. Then, it randomly selects latent codes from the latent probability distribution to uncover private data. As the latent probability distribution closely mirrors the target privacy data in the latent space, the recovered data effectively leaks the privacy of the target model’s training samples. Extensive experiments conducted on diverse datasets and networks demonstrate that our DBB-MI outperforms state-of-the-art MI attacks in terms of attack accuracy, K-nearest neighbor feature distance, and peak signal-to-noise ratio.

In mobile crowdsensing, task interruptions can cause failures and reduce system stability. Despite the significance of this issue, few studies have addressed task allocation under interruptions. To bridge this gap, we propose IT-STA, an interruption-based stable task allocation algorithm that reallocates interrupted tasks to improve completion rates and maintain system stability. First, an efficient detection mechanism is designed to promptly identify interrupted tasks, ensuring timely intervention. Second, a distributed reallocation strategy is developed to assign interrupted tasks to suitable participants, leveraging a novel individual migration strategy that enables parallel coordination among nodes, ensuring efficient global matching and avoiding suboptimal solutions. Experimental results demonstrate IT-STA’s superiority over baselines in task allocation stability and performance.

Collaborative tasks often require the cooperation of multiple individuals to be completed in mobile crowdsensing (MCS). However, previous task recommendations predominantly focused on individuals rather than groups, making them less effective for collaborative tasks. It is crucial to study the collaborative task recommendation problem in MCS. In this work, we propose an Attention-based Neural Collaborative approach (ANC) for group task recommendation. In particular, a grouping method is designed based on participant abilities to form groups that meet the needs of collaborative tasks. Meanwhile, a dual-attention mechanism is constructed to aggregate member preferences and enhance the representation of tasks and groups. The neural network-based collaborative filter mechanism is employed to generate top-$K$recommendation lists. Experimental results, based on two real-world datasets, demonstrate that ANC outperforms others, validating its effectiveness and feasibility.

Mobile crowdsensing (MCS) refers to a group of mobile users utilizing their sensing devices to accomplish the same sensing task. However, in vehicular networks, how to evaluate the reliability of sensing vehicles and achieve lightweight privacy preservation are urgent issues. Therefore, this paper proposes a lightweight privacy preservation scheme with efficient reputation management (PPRM) for MCS in vehicular networks. Specifically, we design a lightweight privacy-preserving sensing task matching algorithm which can preserve the location privacy, identity privacy, sensing data privacy, and reputation value privacy while reducing communication and computation overheads of sensing vehicles. In particular, to prevent reputation values from being forged and select reliable sensing vehicles, we present a privacy-preserving reputation value equality verification algorithm to verify reputation values and a privacy-preserving reputation value range proof algorithm to choose sensing vehicles. Afterwards, a three-factor reputation value update algorithm is constructed to efficiently and accurately update the reputation values for sensing vehicles. Simulations are conducted to demonstrate the performance of the PPRM scheme, and the results show that the PPRM scheme significantly outperforms the existing schemes in security and robustness aspects.

Based on the Threshold Predicate Encryption (TPE), the biometric authentication schemePassBioaims to correctly authenticate genuine end-users without leaking their biometric privacy information. However, this article proposes two impersonation attacks toPassBioby merely sending very few query messages. Specifically, an attacker is able to cheat the authentication server with probability 50% by sending the server a random query, or almost 100% by sending the server a collusion of old genuine queries, without being identified. Moreover, in order to defeat the impersonation attacks, this article presents a Verifiable Threshold Predicate Encryption (VTPE) scheme which includes three components: (1) a multi-segment TPE for reducing the computational cost and communication overhead significantly; (2) a segment-wise watermarking for defeating the random attacks; and (3) a challenge-response mechanism for defeating the replay and collusion attacks. In addition, the watermarking also creates a secure channel between the querying user and the server. The experiments on both simulated feature vectors and real face images demonstrate that the present attacks and countermeasures are effective and efficient.

In an acceleration-based Secure Device Pairing (SDP) scheme, two unauthenticated devices continuously measure their own acceleration. If the similarity of their measurements are sufficiently high, the devices will build a secure communication channel assume that it is hard for any attacker to estimate their measurements in real-time. This paper demonstrates that the assumption does not hold and further proposes an effective Man-in-the-Middle (MitM) attack on acceleration-based SDP schemes. That is to say, an MitM adversary is able to quickly estimate the acceleration measurements of the target devices with automatic visual tracking technologies, and then compromise the device’s communication channel by impersonating the target devices with the estimated measurements. The present attack is extensively evaluated on acceleration-based SDP schemes in indoor and outdoor environments. The evaluation results show that the device’s acceleration can be estimated with high accuracy in real time. Thus, the present MitM attack is practical to defeat the acceleration-based SDP schemes.

The next-item recommendation problem has received more and more attention from researchers in recent years. Ignoring the implicit item semantic information, existing algorithms focus more on the user-item binary relationship and suffer from high data sparsity. Inspired by the fact that user's decision-making process is often influenced by both intention and preference, this paper presents a SequentiAl inTentiOn-aware Recommender based on a user Interaction graph (Satori). In Satori, we first use a novel user interaction graph to construct relationships between users, items, and categories. Then, we leverage a graph attention network to extract auxiliary features on the graph and generate the three embeddings. Next, we adopt self-attention mechanism to model user intention and preference respectively which are later combined to form a hybrid user representation. Finally, the hybrid user representation and previously obtained item representation are both sent to the prediction modul to calculate the predicted item score. Testing on real-world datasets, the results prove that our approach outperforms state-of-the-art methods.

Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor–critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.

Automatic image annotation is one of the research fields helping to extract the meaning of images, which aims at the production of a set of semantic annotations for an image to help better present the concept. Over the past few decades, researchers have developed many approaches for automatic image annotation. Nevertheless, previous studies have not fully accounted for visual features and annotated features. Therefore, it is still possible to achieve a better annotation performance by combining visual and annotated information. In this study, we aim to associate multiple semantic tags with a given image. In particular, we detect how to obtain the image annotation by utilizing visual and annotated information. To take advantage of visual information, we first designed a modified neural network method to acquire the features of the image content. In addition, to obtain the annotated features, we exploit an aggregated network embedding approach that consists of annotation embedding, social embedding, profile embedding, and semantic embedding. Finally, to produce an accurate image annotation, we integrate the two aforementioned methods, that is, combining the visual and annotated information, to build a unified cooperative training framework. The experimental results on three real-world datasets clarify that our presented method is superior to the currently popular image annotation approaches.

This paper investigates the secure transmission in multiple access wiretap channels, where multiple legitimate users transmit private information to an intended receiver in the presence of multiple eavesdroppers. In order to improve security, we propose a novel cooperative jamming scheme, in which users do not share channel state information (CSI) but the legitimate channels will not be degraded by the artificial noise. The basic idea is to make each user exploit its own CSI in two slots to design artificial noise, so that the intended receiver can eliminate all the artificial noise but the eavesdroppers cannot. In this process, the interference between users plays a key role to achieve security, because it guarantees that the artificial noise from different users helps each other. We consider the non-collusion and collusion of eavesdroppers and analyze the secrecy performance for both scenarios. We adopt the secrecy sum-rate as the main metric, and show that positive secrecy sum-rate can be achieved by using the proposed scheme. Especially, we observe that when eavesdroppers collude and their additive white Gaussian noise (AWGN) close to zero, the number of users must not be less than twice the number of eavesdroppers to ensure positive secrecy sum-rate. Finally, simulation results are provided to corroborate our theoretical findings.

Vehicular ad-hoc networks (VANETs) have recently attracted considerable attention from both industry and academia for improving road safety and traffic efficiency. Trust modeling plays a significant role in VANETs, however, the existing trust models cannot primely conform to the characteristics of VANETs. This article proposes a novel trust cascading-based emergency message dissemination (TCEMD) model which incorporates the entity-oriented trust values into data-oriented trust evaluation in an efficient manner. In the proposed model, when an emergency event (e.g., an obstacle in front of the road) occurs, the emergency messages can be disseminated among the nearby vehicles in a trust cascading manner, where the entity-oriented trust values (which are evaluated and updated by leveraging the trust certificates and are contained in the messages) are adopted as important weights. Subsequently, the theoretical analysis for the robustness against several kinds of attacks and malicious behaviors, failure tolerance features, compatibility for several kinds of special situations, and incentive mechanisms in the TCEMD model are detailed. Afterwards, a series of simulations and analyses are conducted in a typical highway environment, and the results reveal that the proposed model significantly outperforms the existing models in several cases.

Sentiment analysis of social multimedia data has attracted extensive research interest and has been applied to many tasks, such as election prediction and products evaluation. Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multimodal data. Different modalities usually have information that is complementary. Thus, it is necessary to learn the overall sentiment by combining the visual content with text description. In this article, we propose a novel method—Attention-Based Modality-Gated Networks (AMGN)—to exploit the correlation between the modalities of images and texts and extract the discriminative features for multimodal sentiment analysis. Specifically, a visual-semantic attention model is proposed to learn attended visual features for each word. To effectively combine the sentiment information on the two modalities of image and text, a modality-gated LSTM is proposed to learn the multimodal features by adaptively selecting the modality that presents stronger sentiment information. Then a semantic self-attention model is proposed to automatically focus on the discriminative features for sentiment classification. Extensive experiments have been conducted on both manually annotated and machine weakly labeled datasets. The results demonstrate the superiority of our approach through comparison with state-of-the-art models.

Today's data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a Heterogeneous netwOrk Policy Enforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be NP-Hard. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as Branch-and-bound optimization but is 3 orders of magnitude more efficient.

Summary In delay tolerant networks, most of the existing message forwarding algorithms prefer to deliver messages to the nodes with a higher popularity or centrality in the hope of maximizing the delivery ratio or minimizing the end‐to‐end delay. This forwarding scheme is prone to cause unfair load distribution and further lead to network congestion, overlooked in the previous work. In this paper, we discuss the network congestion from a community perspective and take it into account the design of message forwarding algorithms. We first put forward a novel distributed community detection approach, which could track the evolution of communities. Based on the identified communities, we develop a congestion avoidance mechanism to divert the load away from the congested areas to the alternative custodians and further present a congestion‐aware message forwarding algorithm where messages can avoid being transmitted to the congested nodes. We finally evaluate the effectiveness of distributed community detection and congestion‐aware message forwarding through the extensive real‐trace driven simulations. Copyright © 2015 John Wiley & Sons, Ltd.

In Delay Tolerant Networks (DTNs), the connections between mobile nodes are always disrupted and constant end-to-end paths rarely exist. In order to cope with these communication challenges, most existing DTN routing algorithms favour the “multi-hop forwarding” fashion where a message can be forwarded by multiple relay nodes in the hope that one of the employed relay nodes can deliver the message to the destination node. Since aggressively employing relay nodes may incur the intolerable delivery cost in DTNs, it is meaningful to design a cost-efficient routing algorithm that can achieve a high delivery performance. In this paper, we first design a novel delivery metric to measure the forwarding capability of nodes. Then, we utilize small-world properties to design the principles of relay node selection, e.g., limiting the number of relays and finding the appropriate relay nodes, and further develop a cost-efficient social-aware forwarding algorithm called TBSF. Extensive simulations on real mobility traces are conducted to evaluate the performance of TBSF, and the results demonstrate its efficiency and usefulness.

In mobile social networks (MSN), with the aim of conserving limited resources, egotistic nodes might refuse to forward messages for other nodes. Different from previous work which mainly focuses on promoting cooperation between selfish nodes, we consider it from a more pragmatic perspective in this paper. Be specific, we regard selfishness as a native attribute of a system and allow nodes to exhibit selfish behavior in the process of message forwarding. Apparently, selfishness has a profound influence on routing efficiency, and thus novel mechanisms are necessary to improve routing performance when self-centered nodes are considered. We first put forward a stateless approach to measure encounter opportunities between nodes, and represent forwarding capabilities of nodes by combining the acquired encounter opportunities with node selfishness. We then quantify receiving capabilities of nodes based on their available buffer size and energy. Taking both forwarding and receiving capabilities into account, we finally present a forwarding set mechanism, which could be deduced to a multiple knapsack problem to maximize the forwarding profit. Consequently, we take all the above studies into the design of a context-aware message forwarding algorithm (CAMF). Extensive trace-driven simulations show that CAMF outperforms other existing algorithms greatly. In fact, it achieves a surprisingly high routing performance while consumes low transmission cost and resource in MSN.

DTNs are prone to congestion due to limited resource on each node and unpredictable end-to-end delay. We aim to develop an effective congestion control mechanism in this paper. For this purpose, we first identify a list of major congestion factors by analyzing the causes of congestion. We then model the congestion control as a multiple attribute decision making problem (MADM), in which the weight of congestion factors is measured by an entropy method. To solve this problem, we present a MADM-based congestion control mechanism that determines a set of forwarding messages and its transmission order on each encounter event. Moreover, we design a buffer management scheme that deletes messages whose removal would incur the least impact to the network performance when the buffer overflows. Extensive real-trace driven simulation is conducted and the experimental results finally validate the efficiency of our proposed congestion control mechanism.

Social-based routing protocols have shown their promising capability to improve the message delivery efficiency in Delay Tolerant Networks (DTNs). The efficiency greatly relies on the quality of the aggregated social graph that is determined by the metrics used to measure the strength of social connections. In this paper, we propose an improved metrics that leads to high-quality social graph by taking both frequency and duration of contacts into consideration. Furthermore, to improve the performance of social-based message transmission, we systematically study the community evolution problem that has been little investigated in the literation. Distributed algorithms based on the obtained social graph are developed such that the overlapping communities and bridge nodes (i.e., connecting nodes between communities) can be dynamically detected in an evolutionary social network. Finally, we take all the results above into our social-based routing design. Extensive trace-driven simulation results show that our routing algorithm outperforms existing social-based forwarding strategies significantly.

In Delay Tolerant Networks (DTNs), the connections between mobile nodes are intermittent and constant end-to-end paths rarely exist. In order to achieve high delivery ratio, most existing DTN routing algorithms favor the ``multi-hop forwarding'' fashion where a message can be forwarded by multiple relay nodes in the hope that one of the employed relay nodes can deliver the message to the destination node. Since aggressively employing relay nodes may incur intolerable delivery cost to resource-constrained mobile nodes in DTNs, it is significant to design a cost-efficient routing protocol that can achieves high delivery ratio. In this paper, we first utilize the small-world feature to limit the maximum forwarding hops in a reasonable way and then propose a greedy relay node selection strategy. A cost-efficient social-aware forwarding algorithm called TBSF (the-best-so-far) is then presented. Extensive simulations on real mobility traces are conducted to evaluate the performance of TBSF. Simulation results show that, in comparison with several well-known routing algorithms, TBSF can achieve high performance in terms of delivery ratio and delivery delay while with much lower delivery cost.