Authentication and Key Agreement (AKA) are essential for UAV networks operating in open and hostile environments, as they assist in preventing common threats such as impersonation and replay attacks. However, traditional protocols often rely on a centralized server for key and identity management, creating risks of key leakage and a single point of failure. To address these issues, we propose a blockchain-based decentralized authentication mechanism that remains effective even under partial node compromise. Our design adopts Physical Unclonable Functions (PUFs) in place of key-based authentication to mitigate key leakage risk. To mitigate machine learning (ML) attacks inherent in existing PUF-based protocols, we design a lightweight Encrypted Randomized Challenge-Response Pair (ERCRP) structure, which incorporates external randomness to obfuscate underlying PUF mapping. Meanwhile, to address CRP leakage in prior centralized schemes, we combine Shamir's Secret Sharing and blockchain for secure CRP management. Specially, we introduce a decoupled design that separates interaction-intensive secret reconstruction process from blockchain consensus to ensure high efficiency. Finally, we develop a lightweight commitment-based management mechanism to prevent unauthorized CRP consumption and reuse from malicious authentication attempts. Additionally, the protocol provides UAV identity untraceability via dynamic identity updates. Comprehensive formal and informal security analyses, together with comparative performance evaluations, demonstrate the protocol's strong security guarantees and practical efficiency.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm of edge model caching. In this paper, we develop a novel model placement framework, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To tackle this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with a $\left(1-ε\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

Malicious traffic detection is an important technique for network management, assisting network administrators in identifying malicious hosts and activities. With the growing proportion of encrypted traffic, recent studies incorporate Machine Learning (ML) and Deep Learning (DL) methods. These methods can achieve effective classification but rely heavily on large-scale and high-quality datasets. Due to the coarse-grained traffic collection at the level of hosts or switches in existing datasets, the introduction of noisy traffic degrades the performance of malicious traffic detection models. In this paper, to tackle the efficacy challenge caused by noisy traffic, we propose a traffic dataset purification framework named PureFlow. Specifically, PureFlow adopts an unsupervised clustering algorithm to categorize the collected traffic based on their sources, and thus overcome the feature confusion caused by mixing noisy traffic from different sources. With a set of autoencoders based on reconstruction loss, PureFlow can distinguish the feature differences between noisy and valid traffic. We conduct extensive experiments on public datasets. The results show that PureFlow can effectively filter noisy traffic and significantly enhance the performance of several malicious traffic detection models without additional modifications, achieving average accuracy and F1-score improvements of 5.58% and 0.056, respectively.

Low earth orbit satellite networks (LEOSNs) are increasingly favored for providing ubiquitous Internet access. However, the dynamic characteristics in LEOSNs pose two challenging issues to congestion control algorithms (CCAs) in transport layer: 1) time-varying link capacity when links remain connected, leading to a continuous mismatch between CCA's sending rate and capacity, and 2) brief but significant link interruptions during satellite handovers. To address these issues, this paper proposes a two-phase CCA called Creo, in which each phase tackles one of the two aforementioned issues in LEOSNs individually. In Creo's connected phase where links remain connected, we design a deep reinforcement learning framework, which captures complex patterns of highly variable link capacities in LEOSNs to generate dynamically adaptive congestion control strategies. In Creo's handover phase where links suffer interruptions, we introduce a handover-aware process, which leverages cross-layer notifications to notify TCP sender in advance of handover occurrence to instruct sender when to stop and resume sending at precise timestamps. Extensive simulation results show that, compared to other CCAs, Creo consistently tracks time-varying capacity, reduces average handover recovery time by 60.5%, and overall achieves a 55% average throughput improvement while maintaining low latency and low delay jitter.

In recent years, multi-hop Quantum Key Distribution (QKD) network has been proven as a promising solution through rigorous practices to provide end-to-end key exchange service for arbitrary communication parties. However, existing decentralized solutions still face critical challenges including consistency and fairness that stem from storable nature of quantum key material. Thus, in this paper, we first devise a Key Management and Service (KM&S) framework for decentralized multi-hop QKD networks, which provides functional decoupling and pipeline processing to guarantee flexibility and compatibility for practical implementation. After that, to address consistency and fairness challenges during end-to-end key exchange service, we focus on two aspects including local key management and end-to-end congestion control, and respectively propose an elastic key supply rate control scheme named AUTO and a Capacity Probing-driven Backpressure Flow Control (CP-BFC) scheme. Furthermore, we construct an experiment platform equipped with realistic QKD devices based on China metropolitan QKD network topology to implement the proposed framework and schemes, and conduct extensive experiments compared to representative schemes in existing studies. The experimental results show that AUTO&CP-BFC significantly outperforms representative schemes in terms of consistency and fairness.

Quantum communication networks enable advanced quantum applications through remote entanglement distribution among source-destination pairs. Despite efforts to optimize entanglement distribution, fairness in multi-request scenarios has been neglected, potentially causing issues like “request starvation”. To address such issue, this paper concentrates on the unique properties of entangled systems and introduces a process-oriented fairness metric, i.e., expected throughput, departing from conventional approaches used in classical networks. Furthermore, we propose an entanglement distribution scheme named Fair-EAS, which prioritizes entanglement allocation and selection for batching multiple requests to maximize overall throughput while maintaining max-min fairness. To facilitate a convenient solution, we transform the nonlinearity of the problem into an equivalent linear programming formulation and decouple the solution into offline and online phases. In the offline phase, we design a multi-round water-filling-like optimization algorithm to determine the optimal path set for predicting entanglement allocation. In the online phase, we introduce an adaptive compensation algorithm and an entanglement “fragment” exhaustion algorithm to dynamically adjust the path set based on successfully generated entangled pairs. Comprehensive simulations show that Fair-EAS outperforms the existing schemes in terms of fairness by significantly enhancing the minimum throughput and throughput deviation among multiple requests while maintaining an overall throughput close to the optimal level.

The high mobility in Vehicular Ad-hoc Networks (VANETs) significantly affects the reliability of data transmission. To solve this problem, Named Data Networking (NDN)-based VANETs are proposed, utilizing in-network caching and named-based forwarding to overcome the dual challenges of mobility and connectivity. Although in-network caching enhances content availability, a strategy that accurately locates and efficiently utilizes the cached content in VANETs with highly dynamic environments is still lacking. In this paper, we propose a novel NDN-based cache-aware adaptive forwarding (CAAF) strategy for VANETs. CAAF proactively predicts content locations and ensures reliable content retrieval by adaptively selecting forwarding nodes that prioritize fast delivery and stable transmission. Specifically, we design a content information table for each vehicle to record information about the Interest packets it receives. Furthermore, these tables are updated periodically across all vehicles and a prediction model is used to predict real-time in-network caching during the update interval. Subsequently, we execute a filter mechanism to sieve candidate forwarding vehicles that satisfy both the accessibility and stability requirements. These candidates are then evaluated using a multi-attribute decision-making method across diverse parameters to determine the optimal forwarding node. Our extensive simulation results demonstrate that the proposed CAAF outperforms the state-of-the-art forwarding strategy regarding content retrieval delay and Interest satisfaction ratio across diverse scenarios.

Multipath TCP (MPTCP) has gained popularity to enhance data transmission. From the last decade, proposed MPTCP enhancement schemes for congestion control, path management, and packet scheduling, have been used to benefit transmission performance. However, despite their efforts, they are exigent with a comprehensive understanding of real-world performance to guide the implementation of MPTCP to a more complex wireless network. To that end, we conduct a measurement-driven study of MPTCP enhancement schemes, providing insights and in-depth demonstrations of their performance with a comprehensive real-world platform. Our finding indicates that the enhancement schemes struggle to consistently maintain high performance at all times. One can achieve optimal efficiency in its specific scenarios, but suffers extreme degradation at times. To eliminate this transmission uncertainty in wireless networks, we further propose an adaptive control framework OLSch to integrate different schemes, emphasizing their strengths to provide consistently high performance. To be specific, OLSch is implemented with different scheduling schemes and leverages an online-learning-driven approach to choose one that best fits the current network conditions. Evaluations show that OLSch obviously improves the stability of transmission in harsh network scenarios, eliminates performance degradation, and increases the 95% tail throughput by 1.45×-2.39×.

Congestion control (CC) is crucial in data center networks (DCNs), providing high throughput and low latency transmission services for diverse applications. Existing CC schemes typically rely on iterative rate adjustment at ends, and suffer from performance issues such as slow convergence, throughput fluctuations, and fairness defects. Explicit rate control (ERC) promises to address these challenges by allowing switches to directly allocate rates for each flow, freeing senders from heuristic detection of available bandwidth. However, current ERC-based schemes employ inefficient feedback control to regulate the allocated rates, resulting in sub-optimal performance. In this paper, we propose RGuide, a fast and accurate CC scheme based on ERC. RGuide can calculate accurate fair share rates in real-time at switches with the consideration of low latency, and utilize the rate to guide host adjustments instead of the need for end-to-end iteration processes. We meticulously design the ERC trigger conditions, enabling switches to recognize the different congestion states of flows and rectify flows that deviate from the fair share rate at sub-RTT timescales. We conduct actual testbed experiments and extensive simulations to evaluate RGuide comprehensively. The results demonstrate the significant advantages of RGuide in terms of convergence speed, throughput stability, and fairness.

Quantum Key Distribution (QKD) technology leverages its inherent security advantages to ensure information-theoretic security for data transmission in networks. However, existing QKD networks still face critical challenges, including network congestion that stems from limited key resources and uneven resource allocation methods. Thus, in this paper, we focus on the issue of network congestion caused by bottleneck links and aim to achieve load balancing. Considering the limited key resources, we first introduce the key resource utilization ratio as an indicator of bottleneck links and formulate the resource allocation problem as an Integer Linear Programming (ILP) problem. To deal with the complexity of the ILP problem, especially in large-scale network scenarios, we design a heuristic algorithm that can obtain near-optimal solutions within polynomial time. Finally, we implement the proposed key resource allocation scheme in various real-world network topologies using a full-stack quantum network simulator. Compared to the existing algorithms, extensive results show that our method can reduce key resource consumption by up to 50% on bottleneck links and improve the robustness of QKD networks when facing burst quantum key agreement requests.

The entanglement transport protocol with a connection-oriented mode ensures the reliable distribution of remote entanglement by reserving dedicated resources on the selected path for users in a quantum network. In most existing protocols, entanglement generation and resource allocation operate with the support of global network-synchronized time slot. However, such synchronization in a large-scale quantum network is challenging, and the idealized time slot model is not conducive to continuous and concurrent requests. Meanwhile, different link performance in memory capacity and entanglement generation rate brings out critical issues, such as long distribution delay and low resource utilization, which has not been adequately addressed by the existing protocols relying on a heuristic adoption of TCP-like transport modes. In light of these observations, we propose a dynamic rate matching-based entanglement transport protocol called DRM-ETP, which allocates different memory units on each link along an entanglement distribution path. Moreover, DRM-ETP incorporates periodic forward and backward interactions to implement fine-grained feedback and a dynamic memory allocation based on priority differentiation. These mechanisms mitigate congestion and unfairness arising from resource contention among burst requests on shared links. Extensive simulation results demonstrate that DRM-ETP significantly outperforms the existing protocols in terms of throughput and resource utilization, with less distribution delay and higher fidelity. Moreover, DRM-ETP exhibits rapid and fair convergence when handling burst requests. Our study opens up possibilities for deploying efficient entanglement transport in quantum networks, thereby holding the promise of enhanced compatibility and novel functionality.

Quantum key distribution (QKD) networks can provide information-theoretically secure key distribution between distant end nodes through key relaying. In QKD networks, the key relay protocol is vital since it provides the coordination specifications between nodes for key relaying and thus directly determines the performance, especially as the network scale expands. However, most existing protocols adopt a synchronous contend-and-relay approach, where the contention and consumption of quantum keys occur simultaneously, neglecting the storable nature of quantum keys and presenting significant challenges in reliability and quantum key utilization. To tackle these challenges, in this paper, we propose an asynchronous key relay protocol (AKRP). AKRP considers the storable nature of quantum keys, and adopts a reserve-then-relay approach to achieve lossless and zero-queuing key relaying through precise management of quantum keys and requests. On this basis, to further improve the performance of the proposed AKRP, we design two enhanced mechanisms, i.e., collision detection and resolution mechanism and multipath routing extension. The former enhances the consensus efficiency of AKRP and provides fine-grained key utilization on each link, and the latter utilizes quantum keys on possible relay paths and thus effectively copes with quantum key exhaustion. By conducting extensive experiments on a semi-physical real QKD network platform, results demonstrate that AKRP is superior to existing schemes in terms of end-to-end key throughput, quantum key consumption, and relaying latency.

Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.

Nowadays, Quantum Key Distribution (QKD) has garnered widespread attention due to its ability to provide symmetric secret keys with information-theoretic security in point-to-point communication. Additionally, key relay technology has been introduced to complete end-to-end key distribution between remote parties by consuming keys in the intermediate links. The routing problem of selecting paths for key relay technology becomes crucial as it directly impacts the network's performance. In this paper, we focus on addressing the routing problem for a specific scenario to serve applications with real-time requirements. Real-time is a critical necessity for supporting various applications, such as video chatting and calling. To satisfy real-time requirements and achieve Quality of Service (QoS) provision to guarantee the completion time, we introduce a distributed routing protocol called Distributed Routing Protocol Based on Key Reservation (Q-RoKR). It reserves keys in advance along the selected path, thereby satisfying real-time requirements. We also propose a priority-awareness mechanism to address resource competition and make efficient use of keys in links. Extensive experiments demonstrate that our protocol effectively meets the real-time requirements and significantly improves throughput. Furthermore, our key efficiency approaches the optimal bound when compared to other comparison schemes.

Remote entanglement distribution in an efficient and reliable manner, especially in the context of a large-scale quantum network with multiple requests, remains an unsolved challenge. The key difficulties lie in achieving spontaneous and precise control over the entanglement distribution procedure, as multiple nodes need to reach a consensus on how to perform it. From the network aspect, allocating link-layer entangled pairs as resources to achieve high efficiency is also challenging. To address these issues, we propose a decentralized Reliable Entanglement Distribution Protocol (REDP) for large-scale networks. The protocol operates in a Forward-Backward Propagation (FBP) manner, where consensus is reached hop-by-hop and disseminated to all nodes on the path. We further use probabilistic analysis and quasi-static modeling to seek the fairness and efficiency of the network based on the above transmission model. Accordingly, we introduce a Source Window Strategy (SWS) and an Entanglement Allocation Strategy (EAS) to assign sending windows and allocate resources for multiple requests, ensuring a high level of fairness and efficiency from a network perspective. Through systematic simulations involving both classical and quantum communication protocols, we demonstrate that REDP outperforms existing approaches in terms of fairness, throughput, and fidelity performance.

The utilization of Multi-path TCP (MPTCP) has been demonstrated to provide superior transport-layer support for data center networks (DCNs) due to its exceptional resource utilization and load-balancing capabilities. However, the substantial path diversity can make it challenging to utilize network resources to their full potential in DCNs. This paper focuses on studying the resource allocation issue of MPTCP from a resource optimization perspective. Based on theoretical analysis, we propose FMPTCP, which uses a feedback-based congestion control algorithm (FCC) and a feedback-based multi-path routing algorithm (FMP) to jointly achieve high bandwidth utilization and low round-trip time (RTT) in DCNs. The FCC algorithm utilizes probabilistic explicit congestion notification (ECN) to provide feedback on path congestion degree, and uses a gradient descent method to adjust the congestion window for optimal resource utilization and load balancing under a fixed routing topology. On the other hand, the FMP algorithm employs a hop-by-hop feedback mechanism to notify in-network congestion and path delay information, allowing for transparent multi-path routing for MPTCP flows. Our extensive simulations demonstrate that FMPTCP enables effective network resource utilization, which not only enhances overall throughput but also reduces transmission latency for DCNs.

Searchable Symmetric Encryption (SSE) is a valuable cryptographic tool that allows a client to retrieve its outsourced data from an untrusted server via keyword search. Initially, SSE research primarily focused on the efficiency-security trade-off. However, in recent years, attention has shifted towards range queries instead of exact keyword searches, resulting in significant developments in the SSE field. Despite the advancements in SSE schemes supporting range queries, many are susceptible to leakage-abuse attacks due to volumetric profile leakage. Although several schemes exist to prevent volume leakage, these solutions prove inefficient when dealing with large-scale datasets. In this paper, we highlight the efficiency-security trade-off for range queries in SSE. Subsequently, we propose a volume-hiding range SSE scheme that ensures efficient operations on extensive datasets. Leveraging the order-weighted inverted index and bitmap structure, our scheme achieves high search efficiency while maintaining the confidentiality of the volumetric profile. To facilitate searching within large-scale datasets, we introduce a partitioning strategy that divides a broad range into disjoint partitions and stores the information in a local binary tree. Through an analysis of the leakage function, we demonstrate the security of our proposed scheme within the ideal/real model simulation paradigm. Our experimental results further validate the practicality of our scheme with real-life large-scale datasets.

Sharding significantly enhances blockchain scalability by dividing the entire network into smaller shards that reach consensus and process transactions in parallel. Nevertheless, two new issues emerge with the adoption of sharding. One issue involves the shrinking size of consensus groups, which leads to vulnerability in consensus. Most existing works introduce periodic shuffle mechanisms to mitigate this problem. Nevertheless, these measures necessitate stronger security assumptions and can only offer a probabilistic assurance of consensus security. Another issue is the challenge in processing cross-shard transactions posed by the isolation of shards. Existing approaches utilize two-phase commit (2PC) or relay transaction mechanisms to handle cross-shard transactions. However, these approaches are vulnerable to double cross-shard attacks from malicious shards and are unable to achieve immediate atomicity. In this paper, to address the vulnerable consensus issue and achieve instant atomicity in cross-shard transactions, we design a hybrid consensus mechanism that embeds a lightweight global consensus into parallel intra-shard consensus processes. The global consensus allows all consensus nodes to jointly process cross-shard transactions, achieving cross-shard transaction instant atomicity. It also records shard snapshots to facilitate shard auditing to defend against malicious shards. Furthermore, we consider the performance of the proposed mechanism, and design a dynamic shard management mechanism. The dynamic shard management mechanism reduces transaction congestion and maintains an appropriate number of shards based on the system’s state. We conduct analyses of potential attacks and prove that our approach ensures safety and liveness even in the presence of malicious shards. We also evaluate the performance of our system and compare it with both non-sharded and classic blockchain-sharding systems. The evaluation results demonstrate the efficacy of our approach in dealing with transaction congestion while astutely controlling the number of shards.

As a promising complement to terrestrial cellular networks, satellite networks have recently drawn increasing attention, offering seamless coverage cost-effectively. However, with the rapidly increasing users' demand for multimedia content, how to achieve efficient content transmission seamlessly becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we propose an opportunistic content-aware routing scheme. Our scheme combines the features of in-network caching and content awareness of information-centric networking (ICN) architecture. The basic idea of the proposed scheme is to sense users' requests and find the optimal route solution with the largest potential gain. Moreover, considering the limitation of real-time signaling collection in satellite networks, we design a cached content prediction method. The method is capable of inferring the probability of content being cached based on historical popularity information, providing essential information for measuring potential gains. Extensive simulation results demonstrate that the proposed opportunistic content-aware routing scheme outperforms baseline approaches with significantly reduced delay and traffic consumption.

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.

With the development of datacenter networks (DCNs) towards high bandwidth and low latency, the demands of high-level datacenter applications are heading towards high performance and high reliability, which makes traffic congestion one of the most notable problems in DCNs and brings new challenges to transport protocols. Proactive transport protocols are gaining prevalence due to their ability to provide accurate feedback and precise end-to-end control, while multipath transmission is having a broader application space in the multi-path topology of large-scale DCNs. However, these advanced transport protocols aim to improve their performance by addressing some specific congestion problems, but fail to handle multiple congestion problems caused by incast, high workload and load imbalance. Their performance in terms of flow completion time (FCT), delay, robustness, and balance still has room for further improvement. In this paper, we propose ProactMP, a novel proactive multipath transport protocol for further improvement of datacenter communications. ProactMP utilizes the rich resources of parallel paths in modern DCN and spreads the load across available network paths to improve network efficiency. ProactMP deploys a credit-based bandwidth allocation strategy to achieve low delay and zero packet loss, and overcommits receiver downlinks to ensure high link utilization. We have implemented ProactMP in the Linux system. Our testbed experiments show that ProactMP outperforms the TCP variants, MPTCP variants and a leading proactive transport protocol in FCT, link utilization, fairness and latency.

The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.

Named Data Networking (NDN) stands out as a promising Information Centric Networking architecture capable of facilitating large-scale content distribution through in-network caching and location-independent data access. However, attackers can easily inject poisoned content into the network, called content poisoning attacks, which leads to a substantial deterioration in user experience and transmission efficiency. In existing schemes, routers fail to determine the contamination source of received poisoned content, leading to the inability to accurately identify attacker nodes. Besides, attackers’ dynamic behaviors and network instability could disrupt identification results. In this paper, we propose a Secure and Lightweight scheme against content poisoning attacks based on Probing (SLP), where a proactive and reliable probing protocol is designed to identify adversaries quickly and precisely. In SLP, a router sends specifically chosen interest packets to probe a suspicious node, so that the returned corresponding content can straightly reflect its trustworthiness without other nodes’ interference. In addition, a hypothesis testing algorithm is developed to analyze the returned content, which can exclude the impact of transmission errors and adapt to dynamic attackers. Moreover, we utilize users’ feedback to avoid unnecessary probing costs on unaffected routers, with its reliability guaranteed by an efficient cuckoo-filter-based feedback validation mechanism. Security analysis shows that SLP achieves resistance against content poisoning attacks and malicious feedback. The experimental results demonstrate that SLP makes users hardly be affected by attacks and brings in only slight overhead.

Named Data Networking (NDN), with a receiver-driven connectionless communication paradigm, naturally supports content delivery from multiple sources via multiple paths. In a dynamic environment, sources and paths may change unexpectedly and are uncontrollable for consumer, which requires flexible rate control and real-time multi-path management, still lacking investigations. To address this issue, we propose an Adaptive Multi-source Multi-path Congestion Control (AMM-CC) scheme based on online learning. AMM-CC explores source/path distribution with continuous micro-experiments and abstracts the empirically experienced performance by meticulously designed two-level utility functions. Specifically, AMM-CC enables each consumer to optimize a local transmission-level utility function that fuses multi-source characteristics, including congestion level and source weights. Then, a sub-gradient descent method is designed to adjust transmission rate adaptively and achieve fine-grained control. Moreover, AMM-CC coordinates consumer with the forwarding module to ensure efficient and on-time multi-path management. It enables consumer to determine congestion gap among multiple paths by a path-level utility that sensitively captures changes and congestion on each path. Then, consumer further notifies the forwarding module in achieving precise traffic transferring. We conducted comprehensive evaluations in dynamic scenario with various content distribution using the NDN simulator, ndnSIM. The evaluation results demonstrate that AMM-CC can adapt to flexible content acquisition from multi-sources and significantly improve bandwidth utilization of multi-path compared with state-of-the-art schemes.

With potential advantages over TCP/IP for content delivery, mobility, and security, Named Data Networking (NDN) has become a promising architecture for the next-generation network. However, its poor performance in reliable transmission is still an unsolved problem. Many existing schemes in NDN employ inaccurate retransmission timeouts calculated with RTTs from diverse content sources to detect packet loss, which is lagging and may deteriorate transmission performance. Besides, after identifying the loss, the consumer costly resends the request to recover it, further increasing recovery time. In this paper, we propose an in-network Proactive Loss Recovery (PLR) scheme, which provides an efficient in-network method for timely detection and proactive recovery of lost packets. Deployed on each router, PLR detects the loss by monitoring queue status and sends high-priority explicit feedback to notify consumers of loss events timely. Meanwhile, lost packets are stored in each router's cache and will be retransmitted at an adaptive rate based on the detected remaining bandwidth. The simulation shows that PLR can vastly reduce the number of retransmissions on consumers, and the content completion time can be decreased by up to 21.8% compared with the baseline.

With Remote Direct Memory Access (RDMA) extended to commercial Ethernet, modern Data Center Networks (DCNs) carry both traditional TCP and RDMA, to support diversified application requirements. RDMA flows are guaranteed lossless transmission through Priority-based Flow Control (PFC), while TCP flows are generally lossy traffic with packet loss. However, TCP is prone to excessively occupy the shared buffer, frequently triggering PFC pause frames and overflows at switches, damaging the performance of RDMA, which expose the vulnerability of existing buffer management policies. In this paper, we propose L2BM, a buffer management algorithm for shared-memory switches to support dynamic hybrid traffic. L2BM utilizes the average occupying time of packets in each ingress queues, to perceive the congestion states timely at ingress ports, allocating the ingress pool fairly and flexibly. Based on the perception, L2BM allocates more buffer for ingress queues with faster drain and lower congestion degrees to absorb micro-burst and reduce pause frames, less buffer for long-occupied queues to prevent excessive injection. As a result, L2BM achieves low tail latency, high burst traffic absorption capacity and low buffer occupancy. Evaluations show that L2BM enable to cut the tail latency of RDMA traffic by 50% at high workloads, reduce the buffer occupancy by 40% and decrease average query delay by 57%, while ensuring few PFC pause frames and maintaining good performance of TCP flows.

Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user Mutiple-Input Single-Output systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternating optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. Compared to the traditional single IRS scheme, the proposed scheme can achieve better performance at the range of 2.4-6.4 bps/Hz with the increase in the number of reflecting elements in the multi-user scenarios. We also evaluate the gap between the secrecy rate for our proposed scheme under continuous phase shift/amplitude control and discrete phase shift/amplitude control, and our results show that the secrecy rate obtained from discrete approximation method converges to that achieved from the proposed scheme when increasing the discretization granularity.

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

Data Center Networks (DCNs) suffer from synchronized bursts for network topology and parallel applications, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.

Existing congestion control algorithms for MPTCP that care about only long flow transmission aim at the Congestion-Avoidance (CA) phase and they need a long time to reach convergence states. We verified that the exponential growth of congestion window (cwnd) in the uncoupled Slow-Start (SS) leads to not only unfairness to TCP but also buffer overflow due to burst data. Moreover, these algorithms cannot support fair bandwidth sharing among TCP/MPTCP flows before reaching convergence at the bottleneck, which may reduce the transmission efficiency of short flows and even hurts long flows. In this paper, we propose a Throughput Consistency Congestion Control (TCCC) algorithm consisting of Coupled Slow-Start (CSS) and Aggressive Congestion Avoidance (ACA). To prevent packet loss caused by excessive burst data, CSS couples the increment of subflows’ cwnd and reset the ssthresh value to safely move the flows to CA when it achieves expected throughput. Based on CSS, ACA periodically detects path states and allocates the same throughput increment as the best TCP to subflows to achieve fair bandwidth share in CA. Finally, we implement TCCC in both NS3 and real testbed. The results show that TCCC reduces retransmissions, improves transmission efficiency, and maintains better fairness.

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

Multipath TCP is designed to utilize multiple network paths to achieve improved throughput and robustness against network failure. These features are supposed to make MPTCP preferable to single-path TCP in mobile networks. However, it fails to achieve the expected performance in practice. A key challenge of using MPTCP in mobile networks is how to effectively spread packets over heterogeneous and unstable network paths to mobile devices with limited buffers. If packets are not sent in an effective way, MPTCP may only provide equal or even lower throughput than single-path TCP. Several packet scheduling algorithms have been designed to tackle this challenge. Unfortunately, they still cannot achieve the expected performance in dynamic scenarios such as mobile networks. In this paper, we propose an Online-Learning Assisted Packet Scheduler (OLAPS) to solve the packet scheduling problem by modeling it as a multi-armed bandit problem. Over time, OLAPS can adaptively learn from current network conditions to make the best scheduling policy to provide the highest possible throughput in a dynamic environment. Moreover, when the inbuilt reward monitor detects the mismatch between network conditions and the learned policy, OLAPS aborts the outdated policy and switches to a new one swiftly. We implement OLAPS as a Linux kernel module and evaluate it over a wide range of ns-3 -simulated network conditions. The results show that OLAPS retains MPTCP’s ability to provide higher throughput and also significantly improves the throughput performance of MPTCP when other in-kernel schedulers suffer a dramatic throughput decline.

Multipath TCP (MPTCP) improves the bandwidth utilization in wireless network scenarios, since it can simultaneously utilize multiple interfaces for data transmission. However, with the fast growth of mobile devices and applications, link interruptions caused by handoffs still lead to drastic performance degradation in such scenarios. Typically, a series of packet losses on part of the links will block the transmission of the entire connection when handoff occurs. This paper proposes an Experience-driven Adaptive Redundant packet scheduler (EdAR) for MPTCP, aiming at achieving seamless handoffs in mobile networks. EdAR enables flexibly scheduling redundant packets with an experience-driven learning-based approach in the face of drastic network environment changes for multipath performance enhancement. To enable accurate learning and prediction, both the network environment and the best course of actions are jointly learned via a Deep Reinforcement Learning (DRL) agent, which we design with a hybrid structure to deal with the complexity of system states. Furthermore, both offline and online learning are utilized to allow the agent to adapt to different and changing network environments. Evaluation results show that EdAR outperforms the state-of-the-art MPTCP schedulers in most network scenarios. Specifically in mobile networks with frequent handoffs, EdAR brings$2\times $improvement in terms of the overall goodput.

A QUIC (Quick UDP Internet Connections) protocol is designed to improve Hypertext Transfer Protocol (HTTP) traffic and carries a non-negligible portion of the traffic in the current Internet. As its extension, Multipath QUIC (MPQUIC) provides higher bandwidth and smoother network handover by using multiple network interfaces simultaneously. However, to improve HTTP traffic, there are still some issues not yet carefully addressed in the existing MPQUIC, and packet scheduling is a vital one among the issues. Specifically, existing methods fail to respond to the stream prioritization of HTTP Version 2 (HTTP/2), leading to unsatisfying web page load performance. Besides, managing asymmetric and dynamic network paths is also a challenging issue, which may result in Head-of-Line (HoL) blocking and excessive buffer usage if not effectively handled. In this paper, we present a stream-aware per-packet scheduler, HoL Blocking Eliminating Scheduler (HBES), to improve the performance of MPQUIC in mobile networks. Firstly, HBES provides a fair allocation of aggregated bandwidth for different streams based on their priority. Then, it keeps stream data arriving at the receiver in order by estimating packet arrival time to mitigate HoL blocking and excessive buffer usage. We implement HBES and evaluate its performance in various network scenarios. Experimental results verify the superiority of HBES in reducing stream completion time and buffer occupation over those existing MPQUIC schedulers.

As a promising complement to terrestrial cellular networks, such as 5G/6G, satellite networks have recently drawn increasing attention. However, facing the challenges of the rapidly increasing users' demand for multimedia content, how to achieve efficient data delivery in a dynamic environment becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we consider the Information-Centric Networking (ICN) architecture and propose a content-aware routing scheme. The basic idea of the proposed routing scheme is to leverage the cached content on cache-enabled satellites and find the optimal route solution with maximum net-gains, i.e., how much delay is reduced. Considering the limitation of periodical signaling collection in satellite networks, we also design a cached content prediction model, which can infer the probability that a certain content could be cached according to the content's historical popularity information, to provide necessary information to measure net-gains. Extensive simulation results show that the proposed content-aware routing scheme outperforms the traditional routing scheme with a 20% reduction in terms of content retrieval delay and traffic consumption.

Through Searchable Symmetric Encryption (SSE), a user can make search over encrypted documents that are stored on an untrusted cloud server. Multi-client SSE schemes require that one client can search documents contributed by other clients and upload documents. Nevertheless, existing multi- client SSE schemes implement the fine-grained access control with high complexity. Although fine-grained access control adapts to complex scenarios, it is not necessary anytime and may cause heavy costs over computation in SSE schemes. Moreover, it is crucial to support documents updating and forward privacy. To combat that, we design a multi-client SSE scheme with efficient access control over dynamic encrypted documents. Specifically, we first modify Symmetric Hidden Vector Encryption (SHVE) and utilize Bloom filter to implement the access control, which reduces much of computation overhead. We then employ Oblivious Dynamic Cross-Tag (ODXT) protocol to preserve the forward privacy of our scheme. Finally, the corresponding security and experimental evaluation demonstrate both security and practicality of our scheme, respectively.

Recently, there have been many discussions in mobile crowd-sensing about privacy-preserving truth discovery because of its ability to extract truthful information from noisy or biased sensory data without privacy breaches. However, in practical applications, users (referred to as workers) may report outliers due to device malfunction, malicious workers, etc. These outliers will dramatically impact the accuracy of the truth discovery result. Detecting outliers based on existing privacy preservation schemes will carry an intolerable overhead, dramatically reducing the system's availability. In this paper, we propose our privacy-preserving truth discovery scheme that can detect outliers. Specifically, we adopt an anonymous mechanism to achieve privacy preservation. Since the existing anonymous mechanisms require huge overhead and do not work correctly when some workers exit, they are difficult to be applied in mobile crowdsensing systems. We design a lightweight and robust anonymous mechanism based on the edge computing paradigm. In addition, we eliminate the impact of outliers through outlier detection to achieve robustness of truth discovery results. Finally, we demonstrate the security of our scheme through security analysis and the efficiency of our scheme in terms of computation and communication overhead through extensive experiments.

Software-Defined Networking (SDN) is a new and highly flexible network architecture, but the bottleneck between the control plane and the data plane makes it vulnerable to the control plane saturation DoS attacks. When the attack happens, traditional schemes in DoS scrubbing agent use a binary classification and a First In First Out (FIFO) queue to filter attack flows. However, this scheme is inimical to the end-to-end latency of benign traffic. To tackle this issue, we propose LLDM, leveraging a dynamic priority scheme and a priority queue to detect, mitigate the attacks while ensuring low latency for benign traffic. After detecting the attack, LLDM leverages a two-phase scheme for mitigation. First, LLDM marks packets from the ports under attack as suspicious and migrates them to the mitigation agent. Then, the dynamic priority manager assigns each packet a priority corresponding to its legality, which is used in the priority queue for DoS scrubbing. We evaluate LLDM in a simulation SDN environment. The experimental results show that LLDM can reduce 90.4% of the queuing delay compared with the traditional scheme under a 5000 Packets Per Second (PPS) attack, and it is also resistant to more sophisticated attacks. Under the high rate attack of 50000 PPS, LLDM installs a flow rule for legitimate traffic in 0.2 seconds. Moreover, for benign HTTP requests, LLDM can keep the request time at 1.39 seconds.

In Software-Defined Networking (SDN), the controllers implement flexible and scalability networking policies by installing different flow rules. Each rule matches a specific class of flows, instructs the switches to execute actions, and then expires when they finish their tasks. OpenFlow introduces the timeout mechanism to manage these flow rules. However, finding a reasonable timeout value becomes a difficult problem for the network managers. When a relatively small timeout value is given to an elephant flow, the rule expires early, introducing extra cost for the controller and long latency for the matching flow, respectively. On the contrary, a large timeout value for a mice flow makes a rule occupy the switch memory too long, wasting the caching memory and causing the flow table prone to overflow. Therefore, it is necessary to allocate appropriate timeouts for different flows dynamically. In this paper, we achieve this goal with real-time traffic monitoring and heuristic algorithms. By considering different network loads and designing corresponding dynamic timeout algorithms for different scenarios, we make full use of the advantages of SDN to improve the utilization rate of the switch memory and save the controller resources. Further, we implement our scheme in a simulation SDN platform and evaluate the algorithms with the public datasets. Experiments show that our scheme has low control overhead and is memory efficient compared with current mechanisms.

E-voting plays a vital role in modern social life. However, traditional e-voting systems usually rely on a trusted third party and therefore non-verifiable and prone to a single point of failure. In recent years, many researchers have tried to turn to blockchain to eliminate the vulnerabilities of e-voting systems. However, blockchain-based e-voting brings new problems in protecting voters’ privacy and ballots’ confidentiality, and causes a great performance degradation. In this paper, we propose AvecVoting, an anonymous and verifiable blockchain-based e-voting scheme, providing both strong security and high performance. Specifically, we utilize threshold encryption and one-time ring signature to protect voters’ privacy and ballots’ confidentiality. Furthermore, to improve the performance, we introduce the concept "counter" to count the ballots. Through the carefully designed RandomSortition and reputation-based PayOff algorithms based on smart contracts, AvecVoting can achieve correct counting even when some counters are untrustworthy. Our security and performance analyses show that AvecVoting provides strong security such as anonymity, non-repeatability, confidentiality, verifiability, etc., and meanwhile overcome the performance issues caused by blockchain and provides good efficiency in both voting and counting stages.

The recent success of Artificial Intelligence (AI) has enabled autonomous driving with better perception capabilities. However, on-ramp merging remains one of the main challenging scenarios for reliable autonomous driving. Within the limited onboard sensing range, a merging vehicle can hardly observe and predict the main road conditions properly, restricting appropriate merging maneuvers. In this poster, we outline ongoing research ideas for reliable and autonomous on-ramp merging assisted by vehicular communications. By jointly leveraging the basic safety messages (BSM) from neighboring vehicles and the surveillance images, a merging vehicle can perform reliable driving via robust multimodal reinforcement learning. Some experimental results are provided to evaluate our idea under the Simulation of Urban MObility (SUMO) platform.

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.

Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.

As a new architecture of Internet infrastructure, Information-Centric Networking (ICN) is mainly designed to effectively handle the rapidly increasing user demand for content delivery through in-network caching. While facilitating the dissemination of content to users and making better use of the network resources, ICN is also vulnerable in that attackers can inject poisoned content into the network and isolate users from valid content sources. The introduction of signature verification in each router can effectively prevent this attack, but it also introduces great computation overhead. Existing schemes in ICN reduce verification overhead from a single routing perspective but do not consider integrating resources within ICN for collaborative content authentication and cyber self-defense. In this paper, we propose a collaborative, secure, and efficient content validation protection framework, named CSEVP, to implement a multi-router collaborative defense mechanism for ICN. On the one hand, we conduct content verification by probabilistically choosing one router involved in the transmission path to offload the computation overhead of content verification from a single router to multiple ones. On the other hand, we adopt bloom filters for routers to record and share verification results to further facilitate a more efficient content validity verification. The security and efficiency analysis shows that our proposed CSEVP can achieve efficient content validity verification among multiple routers with acceptable low communication and storage overhead.

As a promising implementation of Information-Centric Networking (ICN), Named Data Networking (NDN) has potential advantages over the TCP/IP network in content distribution, mobility support, etc. However, the research on NDN is still in its infancy, and congestion control, NDN’s most important functional element, poses many challenges, such as congestion detection, excessive window reduction for non-congested paths, and unfairness. In this paper, we propose an Intelligent Edge-Aided Congestion Control (IEACC) scheme for the NDN network based on Deep Reinforcement Learning (DRL). The proposed IEACC provides a proactive congestion detector that utilizes intermediate routers to transmit accurate congestion information along the path to consumers through data packets. Furthermore, considering the multi-source transmission in NDN, IEACC divides data packets into different congestion degrees by a lightweight clustering algorithm and provides suitable inputs for DRL, thereby obtaining a reasonable transmission rate. Then, it distributes the estimated bandwidth resources to consumers with transmission needs to maintain fairness. Finally, we implement our proposed scheme in the simulation platform and evaluate the performance in different scenarios. The results show that it can improve data transmission rate, reduce packet loss, and maintain fairness compared with others.

Truth discovery is an effective method to infer truthful information from a large amount of sensory data in mobile crowdsensing systems. Privacy-preserving truth discovery schemes require the cloud server not to access each worker's sensory data directly so that the privacy of sensory data can be preserved. In some specific applications such as sparse mobile crowdsensing, workers can only contribute sensory data on a small part of sensing tasks, implying that the information of which tasks are completed by a worker should also be preserved. However, existing privacy-preserving truth discovery schemes do not consider such sparse data scenarios in mobile crowdsensing systems. In this paper, we first identify the privacy issues in truth discovery when sensory data are sparse. To address these issues, we design a privacy-preserving truth discovery scheme by employing the additively homomorphic cryptosystem and additive secret sharing with two non-colluding servers. Through detailed analysis and extensive experiments, we demonstrate that our proposed scheme can satisfy strong privacy-preserving requirements with low computation and communication overhead.

With the proliferation of mobile and wearable devices, mobile crowdsensing (MCS) is becoming a new paradigm for data collection and analysis. To effectively identify truthful information from crowdsensed data without privacy leakage, privacy-preserving truth discovery (PPTD) has gained much attention recently. Existing works either didn't consider real-time applications over data streams or failed to achieve enough efficiency for a large group of workers. In this paper, we propose FPTD, a Fog-aided Privacy-preserving Truth Discovery framework which is secure and efficient in handling real-time applications with a large group of workers. To reduce overhead, we adopt cloud-fog computing architecture to divide the complete worker group into many smaller ones. Then we design a unique secure aggregation protocol SecAgg which can securely and efficiently aggregate inputs from workers in smaller groups. Finally, we give detailed construction of FPTD, an efficient truth discovery framework based on SecAgg for real-time applications. Through extensive experiments and security analysis, we demonstrate that both SecAgg and FPTD are secure and efficient.

Many applications are bandwidth consuming but may tolerate longer flow completion times. Multipath protocols, such as multipath TCP (MPTCP), can offer bandwidth aggregation and resilience to link failures for such applications, and low priority congestion control (LPCC) mechanisms can make these applications yield to other time-sensitive ones. Properly combining the above two can improve the overall user experience. However, the existing LPCC mechanisms are not adequate for MPTCP. They do not take into account the characteristics of multiple network paths, and cannot ensure fairness among the same priority flows. Therefore, we propose a multipath LPCC mechanism, i.e., Dynamic Coupled Low Extra Delay Background Transport, named DC-LEDBAT. Our scheme is designed based on a standardized LPCC mechanism LEDBAT. To avoid unfairness among the same priority flows, DC-LEDBAT trades little throughput for precisely measuring the minimum delay. Moreover, to be friendly to single-path LEDBAT, our scheme leverages the correlation of the queuing delay to detect whether multiple paths go through a shared bottleneck. Then, DC-LEDBAT couples the congestion window at shared bottlenecks to control the sending rate. We implement DC-LEDBAT in a Linux kernel and experimental results show that DC-LEDBAT can not only utilize the excess bandwidth of MPTCP but also ensure fairness among the same priority flows.

Service prioritization brings reasonable allocation of network resources and improves the overall quality of experience (QoE) of users, but it has not been thoroughly investigated in information centric networking (ICN). Existing works lack adaptability and they cannot ensure specific content provider (CP) get well caching service which is one of the most important functions in ICN. In this paper, we firstly propose a service prioritization scheme to flexibly provide different caching services for heterogeneous CPs to improve the overall network efficiency. The main idea is to allocate dedicated cache space for paying CPs and provide prioritized caching service for them, while normal CPs only enjoy the normal caching service. The scheme can be divided into two phases. First, we select a group of nodes with higher importance as core nodes based on network topology, and pair each edge node to a core node following the two-sided many-to-one matching algorithm. Second, we dynamically allocate and manage the dedicated cache space for core nodes. We model the allocation of dedicated cache space and convert it into a convex optimization problem to solve. After that, a practical caching strategy and system design are implemented in the ndnSIM simulator. Finally, we evaluate our scheme and conduct comparative experiments with the most representative work diff-caching, simulation results show that our scheme outperform it in terms of both delay and cache hit ratio.

Multipath TCP (MPTCP) utilizes multiple paths for simultaneous data transmission to enhance performance. However, existing MPTCP protocols are still far from satisfactory in wireless networks because of their loss-based congestion control and the difficulty of managing multiple subflows. To overcome these problems, we redesign the coupled congestion control algorithm and scheduler to boost MPTCP in wireless heterogeneous networks. The main purpose is to promote transmission rate under lossy networks, while also provide stability when networks suffer physical link changes and asymmetric links. In this paper, inspired by Bottleneck Bandwidth and Round-trip propagation time (BBR), we first propose Coupled BBR that utilizes detected bandwidth to adjust the sending rate within an MPTCP connection. Coupled BBR provides high loss tolerance as well as balanced congestion among MPTCP subflows. Then, to further improve the performance, we propose an Adaptively Redundant and Predictive packet (AR&P) scheduler to improve adaptability and keep in-order packet delivery in highly dynamic network scenarios. Based on Linux kernel implementation and experiments in both testbed and real network scenarios, we show that the proposed scheme not only provides high throughput in wireless networks, but also improves robustness and reduces out-of-order packets in some harsh circumstances.

It is a known issue that low-latency communication is hard to achieve when using multiple network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN) simultaneously. A main underlying cause of this issue is that the packets with lower sequence number are stalled on a high-latency path, thus the early arriving packets with higher sequence number become “out-of-order (OFO)” packets. These OFO packets may excessively consume receiver’s buffer, causing long reordering delay and unnecessary packet retransmission. In this paper, we present a novel design of packet scheduling for Multipath TCP (MPTCP), called OverLapped Scheduler (OLS), able to tackle the OFO-packet problem more effectively. OLS can guarantee sufficient throughput on demand of upper layer applications, and utilizes the remaining bandwidth to reduce OFO-packets. To do so, OLS schedules packets according to their arrival time and sends a controlled number of redundant packets to avoid the impact of inaccurate arrival-time estimations due to network jitter. We implement OLS in a Linux kernel, and the experiments show that in asymmetric networks with or without jitter, OLS can effectively reduce OFO-packets and transmission latency while maintaining a sufficient throughput, which makes it fully capable to meet the requirements of applications such as live video streaming.

In order to cope with the explosive growth of mobile traffic, many traffic offloading schemes such as heterogenous networks have been developed to enhance network capacity of the Radio Access Network (RAN). Among them, networking of Low-Earth Orbit (LEO) satellites promises to significantly improve the RAN performance due to its economical prospect and advantages in high bandwidth and low latency. In this paper, by introducing the cache-enabled LEO satellite network as a part of RAN, we propose an integrated satellite/terrestrial cooperative transmission scheme to enable an energy-efficient RAN by offloading traffic from base stations through satellite's broadcast transmission. Considering energy-constraints of satellites, we then formulate a nonlinear fractional programming problem aiming at optimizing transmission energy efficiency of the system. In order to effectively solve this problem, we transform it into an equivalent one, and then adopt iteration and sub-problem decomposition to obtain the optimal solution for each optimization variable, i.e., block placement, power allocation, and cache sharing variable. Numerical results show that compared with traditional terrestrial scheme, our cooperative transmission scheme achieves significant performance improvement in terms of traffic offloading and energy efficiency, especially in an environment of high request consistency degree.

In the context of Service Function Chain (SFC), SFC Mapping (SFCM) problem has a decisive impact on the resource utilization efficiency of physical networks, where node resources and link resources are concerned. However, most existing work on the SFCM problem has not taken into account difference in the amount of node resources and link resources. This kind of difference might result in some nodes that cannot be mapped because of insufficient link resources around these nodes. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by reducing resource fragmentation in physical networks, where SFCM is performed. First and most importantly, we propose a metric called Resource Fragmentation Degree (RFD) to quantify resource fragmentation. The basic idea behind RFD is that the resource availability of a node is determined by the residual link resources around the node. Based on RFD, we formulate the SFCM problem with goal of minimizing resource fragmentation. Furthermore, we also propose an efficient online heuristic to find the optimal mapping strategy. Simulation results show that much more SFC requests can be accepted by reducing resource fragmentation in physical networks and the proposed algorithm achieves more than 25% higher acceptance ratio compared with existing algorithms.

With global coverage abilities, satellite networks are expected to provide users with ubiquitous data services. However, routing in satellite networks faces more challenges due to the satellite movement. Fortunately, the satellite movement can be predicted by orbit calculation. Based on this characteristic, a lot of existing routing algorithms use Temporal Graph Model (TGM) to calculate instantaneous satellite network topologies at discrete times as priori knowledge for routing decision. In this case, high computation cost will be involved. In this paper, we propose a novel Temporal Netgrid Model (TNM) to represent the time-varying satellite network topology. In TNM, the whole space is divided into small cubes (i.e. netgrids) and then satellites can be located by netgrids instead of coordinates. By doing so, TNM reduces the computation complexity from O(N2) to O(N2) compared with TGM. Furthermore, an Earliest Arrival Space Routing (EASR) algorithm is proposed, which attempt to find the earliest arrival paths from the source node to any other reachable nodes with low computation cost. Simulations are performed to validate the effectiveness of the proposed routing algorithm. Results show that EASR algorithm achieves a significant reduction in computation complexity as well as acceptable routing performance in terms of data delivery ratio.

With ability to eliminate the cliff effect and achieve graceful degradation, analog-like transmission such as SoftCast has become a hot research issue for robust image/video delivery over wireless channels. However, it has not been well studied in the case of bandwidth compression, which usually occurs in bandwidth-constrained wireless environments such as satellite communication scenarios. In this paper, we propose an analoglike robust transmission scheme based on Compressive Sensing (CS) for satellite image delivery over bandwidth-constrained wireless channels. The motivation for integration of CS in the proposed scheme lies in the fact that the simple dropping strategy is not optimal for satellite image transmission when bandwidth is insufficient. Considering high information entropy and rich structure information properties of satellite images, we perform an amplitude offset operation and the block-based CS (BCS) procedure to improve the energy efficiency and meet the bandwidth budget respectively. We analyze the system distortion of the proposed scheme and formulate the distortion minimization problem as a resource allocation problem. Then, we propose an efficient two-step strategy to find the optimal solution for bandwidth and power allocation. The simulation results show that the proposed scheme achieves up to 5.5dB gain over the state-of-the-art transmission schemes for satellite image delivery.

With the development of satellite communication technologies and user requirements for pervasive network access, there is a trend to integrate satellites into the terrestrial network infrastructure. Such kind of satellite-terrestrial network is often used for content delivery services as satellites are with wide-area coverage. In terrestrial networks such as Internet, in-network caching has been proved to be an effective method to improve the network performance in terms of throughput and delay. Based on this observation, we involve caches in the satellite-terrestrial networks. Particularly, a two-layer caching model is proposed for content delivery, where caches placed in the ground stations constitute the first caching layer and caches deployed in the satellite forms the second one. On the satellite, to make full use of the broadcast advantage, we set a window to aggregate the requests for the same files from ground stations. These requests will be served by one satellite broadcasting when the aggregation window expires. Our goal is to minimize the downlink and uplink satellite bandwidth consumption, which requires joint caching optimization between the satellite and ground stations. We formulate the joint caching optimization problem as a nonlinear integer programming problem. Furthermore, a caching strategy based on the genetic algorithm is proposed to solve the problem efficiently. The simulation results show that the proposed caching strategy significantly outperforms content popularity based and random caching strategies in terms of satellite bandwidth consumption.