Segment routing (SR) is an emerging architecture that can benefit traffic engineering (TE). Nowadays, TE in SR networks (SR-TE) is often solved as an optimization problem to optimize network performance such as link utilization. As network size grows rapidly, implementing SR-TE suffers from scalability issues, including long computation time, high control overhead and expensive deployment cost. In this paper, we propose Smart Node Selection (SNS), a scalable SR-TE method with learning-based node selection (NS). NS is a recently proposed technique for reducing computation time of SR-TE. It first selects a subset of nodes as candidate intermediate nodes to route traffic, then builds linear programming (LP) models that can be solved efficiently. However, existing NS methods use simple heuristics and consider only network topology, which may lead to unsatisfying network performance. To address this problem, we for the first time formulates NS as a reinforcement learning task, which learns a selection policy to achieve better trade-offs between TE performance and computation time, considering both topology and traffic. Besides, we extend NS with additional selection policies and a customized training algorithm, making it a unified framework for scalable SR-TE, which reduces not only computation time, but also control overhead and deployment cost. Performance evaluations on various real-world topologies and traffic matrices show that SNS significantly reduces computation time and control overhead of existing LP models while offering good network performance, and can also be used in partially deployed SR networks to reduce deployment cost.

With the emergence of next-generation video applications and increasing spatial resolutions, delivering high-quality video is still limited by network bandwidth. Adaptive bitrate (ABR) can select the appropriate bitrate for video streaming based on bandwidth, which can mitigate rebuffering caused by insufficient bandwidth. In comparison to Constant Bitrate (CBR), Variable Bitrate’s (VBR) encoding scheme can achieve the same quality with less bandwidth consumption and is gradually being widely used in ABR streaming. However, the quality of the video is still degraded due to a poor network. Recent research utilizes Super-resolution (SR) in ABR streaming to construct neural Video Quality Enhancement (VQE) systems, thereby improving the quality of video segments downloaded due to insufficient bandwidth. However, SR cannot participate in the downsampling encoding process of videos, which results in the effectiveness of existing SR-based VQE systems being inherently limited due to unavoidable information loss during downsampling encoding. Concurrently, SR’s high computational cost restricts neural VQE systems’ deployment on clients without GPUs. In contrast to the unidirectional workflow of SR, Rescaling can be integrated into the downsampling encoding process of videos, allowing favorable information to be retained for VQE. To implement high-quality real-time VQE for ABR streaming of VBR-encoded videos on CPUs, we propose RePC, a novel neural VQE system framework for optimizing existing neural VQE systems based on Rescaling (Re) for the first time, and Patch Content-awareness (PC). In detail, RePC uses Rescaling instead of SR to achieve better VQE by participating in the video downsampling. We also propose a Video Single-Image Rescaling model, VSIR, to indicate the effectiveness of RePC in quality enhancement. To speed up VQE, RePC designs a PC algorithm to mix interpolation and neural computation based on the practical upsampling ability. Our evaluation results demonstrate quality gains of 0.55–2.96 dB in PSNR and 1.79–3.18 in VMAF with fewer parameters, a speed-up of 15×–286× well up to real-time requirements on CPUs, and Quality of Experience (QoE) improvements of 16.58–26.65 are also achieved in an ABR system under various networking conditions.

Traffic engineering (TE) is fundamental and important in modern communication networks. Deep reinforcement learning (DRL)-based TE solutions can solve TE in a data-driven and model-free way thus have attracted much attention recently. However, most of these solutions ignore that TE is a real-world application and there are challenges applying DRL to real-world TE like: (1) Efficiency. Existing learning-from-scratch DRL agent needs long-time interactions to find solutions better than traditional methods. (2) Safety. Existing DRL-based solutions make TE decisions without considering safety constraints, poor decisions may be made and cause significant performance degradation. In this paper, we propose a safe training approach for DRL-based TE, which tries to address the above two problems. It focuses on making full use of data and ensuring safety so that DRL agent for TE can learn more quickly and possibly poor decisions will not be applied to real environment. We implemented the proposed method in ns-3 and simulation results show that our method performs better with faster convergence rate compared to other DRL-based methods while ensuring the safety of the performed TE decisions.

Segment routing (SR) is an emerging architecture that can benefit traffic engineering (TE). To solve TE in SR networks (we call it SR-TE), linear programming (LP) is often used. But LP methods proposed so far for SR-TE are computationally expensive thus do not scale well in practice. To achieve trade-off between performance and time, we can select a set of nodes as candidates for intermediate nodes to route all traffic instead of considering all the nodes. However, existing node selection methods are all rule-based and only pay attention to the structure of network topology without considering flows, so they are not flexible and may lead to poor performance. In this paper, we for the first time formulate node selection for SR-TE as a reinforcement learning (RL) task. When performing node selection, we consider the impact of both topology and traffic matrix. Also, a customized training algorithm for our task is proposed because existing RL algorithms can not be used directly. Performance evaluations on various real-world topologies and traffic matrices show that our method can achieve good TE performance with much less running time.

Wi-Fi Protected Access II Pre-Shared Key (WPA2-PSK) is a hot way to wireless security in public Wi-Fi networks. It works on a pre-configured passphrase shared with all stations in the same Wi-Fi network. Session keys (e.g., Pairwise Transient Key, PTK) between stations and the access point (AP) are derived from the passphrase. The WPA2-PSK networks can authenticate external stations, however, they fail to guarantee confidential communication if internal attackers own the passphrase in the network since all stations derive their PTK using the same passphrase. To prevent internal stations from eavesdropping the PTK, a secure session key negotiation scheme in WPA2-PSK Networks (SSKNS) is proposed. We introduce a temporary session key (TSK), which is encrypted using elliptic curve cryptography (ECC) and exchanged securely between the station and the AP in the Wi-Fi association process. Through AES algorithm with TSK, the station encrypts its own nonce used to generate the unique PTK in the 4-way process. Our scheme neither modifies the legacy process related to PTK generation nor adds plethoric overhead on excessive protection of all messages. Security analysis and simulations performed in NS-3 demonstrate that by consuming a few computation overheads, SSKNS can effectively provide security level, compared with the existing schemes.

Blockchain can be used to ensure trust in a decentralized environment in which no trusted authority is available. Its original idea is to collect transactions in a block, and to chain the blocks together in such a way that attackers cannot forge the chain if the majority of the network is honest. Since its creation in 2008, blockchain technology has been used broadly in Internet to support decentralized payments, cloud computing, publishing, etc. This work focuses on public permissionless blockchain which neither guards against bad actors nor enforces access control. Named data networking (NDN) uses name-based routing and in-networking caching to support efficient content delivery, making it a promising future Internet architecture as well as a great network technology which can improve blockchain data delivery. Therefore, it is a very necessary task to enable deployment of blockchain applications over NDN. However, NDN is not immediately compatible with typical blockchain, since (permissionless) blockchain applications usually require broadcasting transactions and blocks in real time, which is not supported by the “pull” design of NDN. In this work, we propose BoNDN which enables blockchain applications over NDN. Unlike previous work, BoNDN follows the core design of NDN. We treat each type of blockchain data needed to be broadcast individually. Specifically, we rely on Interest broadcasting to support real-time broadcasting of blockchain transactions, which is small in size and can be brought by an Interest packet. In addition, we propose a subscription-push approach to support broadcasting of blockchain blocks, in which each miner performs subscription, and once a block is generated, the subscribed miner will receive the block.

In today's society, there are plenty of services available, and customers are facing bigger challenge in choosing them than ever before. Therefore, it is important to build a reliable reputation mechanism for selecting a credible service. To address the challenges of reputation evaluation, including the diverse and dynamic natures of services, incompleteness of user feedback, and intricacy of malicious ratings, a High-reliability Multi-faceted Reputation evaluation mechanism for online services (HMRep) is proposed. First, HMRep starts with addressing the incomplete feedback and estimates missing ratings based on both the service quality and a user's rating behavior. Second, HMRep identifies and removes malicious collusive raters and irresponsible raters to improve the accuracy of reputation calculation. Further, the reputation calculation is based on the user credibility and incorporates historical information to reflect the change of the services. Finally, we provide a multi-faceted evaluation method to satisfy some specific needs of customers who are only concerned about a subset of a services features. Experimental results verify the design of HMRep, and reveal HMRep can effectively defend against malicious ratings, and accurately calculate the reputation values of services. HMRep can be applied in lots of sectors for different kinds of services, especially those complex ones.

This paper is concerned with the adaptation to multi-feature preferences on personalized search. In the existing work, the personalized search mainly leverages semantic features extracted from user history and ignores other non-semantic latent features, lets alone adapt to preference distribution on non-semantic features. To tackle this problem, we propose an adaptive model for multi-feature preferences, in which we adapt latent non-semantic features extracted from visited pages to reflect diverse aspects of user preferences. We also utilize a novel algorithm in our model to improve the adaptation for diverse preference distribution on these features for different users. Our experimental results demonstrate that our model can improve personalized search performance by enhanced adaptation to diverse user preferences.

The current BitTorrent-like file sharing systems suffer from peer selfish behaviors. The uncooperative peers can freeload compliant users by free-riding and exploiting. To study the performance of BitTorrent's embedded incentive mechanism against selfishness, a fluid model with three different classes of peers, namely normal peers, exploiters and free-riders, is established. We point out that the current BitTorrent system can not provide an effectively differentiated service in accordance with contribution of peers. Therefore, a reputation based incentive (RBI) mechanism for selfish BitTorrent system is proposed. RBI defines a trust value for each peer associative to its historical performance to the whole system. With the trust value, the choking mechanism is modified to ensure the more trustworthy peers will have more chances to get served. Our simulation study indicates that RBI mechanism can remarkably prevent exploiting behaviors, severely penalize free-riders, and thus result in a fairer allocation of bandwidth among peers.

To counter malicious peers in P2P systems, PeerTrust-like schemes take advantage of similarity between peers to compute service provider's trust value. To derive the similarity, there currently exist some approaches documented which however mostly are interested in algorithm or process enhancement, the analytic study in theory for these PeerTrust-like mechanisms might be missed. This paper focuses on the basic problems in PeerTrust-like trust schemes and makes the following distinctive contributions. It formalizes the generic framework of trust mechanisms in a probabilistic manner; gives the mathematical description for PeerTrust-like trust mechanisms; attempts to figure out some unclear questions: based on PeerTrust-like schemes, how to compute similarity via Euclidean distance; should all feedback be counted in; how to compare PeerTrust-like mechanisms with other distance/similarity computations.

To address the selfishness issue in application-layer multicast, we present a selfishness-aware application-layer multicast (SAM). SAM defines an altruism value for each node associative to its contributions to the system. Nodes are first partitioned into topologically-aware clusters using the binning scheme. Within the cluster, the subtree is constructed to place the nodes with greater altruism value at the higher layer of the tree. As compared to other studies in this area, SAM exhibits innovative advantages in both altruism value computation and multicast tree construction. Firstly, the node's altruism value is generated from the feedback from its parent and children which enables the system to detect the selfish nodes effectively. Peers don't need the extra probe messages to measure the QoS of their neighbors. During the process of tree construction and maintenance, only O(logN) nodes need to be adjusted. Lastly, the altruism value calculation and multicast tree construction are realized in a decentralized manner without any single point of failure. Simulation results show that even with a significant portion of nodes being selfish, SAM is able to build a dissemination tree that provides high overall streaming quality with low control overhead.

The current BitTorrent-like file sharing systems suffer from peer selfish behaviors. The uncooperative peers can freeload the compliant peers by various methods. To prevent the selfishness, this paper presents a file swap scheme, also known as Odd for Even (OFE), as an enhancement to the current BitTorrent. With OFE, the file is segmented into odd and even pieces; the peers swap the pieces according to "odd first, even later" rule. To study the performance of OFE against the selfish behaviors, a modeling for the related factors is established. We suggest that by using an appropriate file segmentation policy, the free-riders who don't make any contribution will undergo a longer download time; the semi-free-riders who only upload odd pieces, deliberately refuse to provide the even ones to save up half upload volume will not get their burden lessened. The experimental results show that OFE can punish these selfish peers effectively.

Data-driven overlay network (DONet) especially works well with live-event streaming because data can be propagated in a relatively continuous way even with node dynamics. However, optimal streaming demands the cooperation of individual nodes. In the real world, some selfish participants which might delay forwarding or stop forwarding data can affect the overall streaming quality. To address the selfishness issue, we propose a selfishness-aware DONet (SA-DONet) in this paper. SA-DONet allows each node associative with an altruism value for its contributions to peers. Based on the altruism value, segment requesting and sending algorithms are designed to ensure the more altruistic nodes will have more chances to be served. The primary characteristic of our mechanism lies in three aspects. Firstly, SA-DONet can discover the selfish nodes in a decentralized manner and adjust the segment sending and requesting strategy dynamically. Secondly, selfish assessment (altruism value) comes from the node's history and doesn't require any extra probe and measuring packets. Lastly, our algorithms remain comparable computing complexity to DONet. Simulation results show that compared with DONet, even with a significant portion of nodes being selfish, SA-DONet can improve the streaming quality of global multicast session with low control overhead.

Session initiation protocol (SIP) has already been accepted as the signaling standard in 3G wireless systems. However, the handover procedure with SIP suffers from undesirable delay in multimedia applications. The performance for real-time mobile communication is decided by multiple factors, we only focus on the handover delay due to host mobility in this paper, especially the delay accumulated during movement detection, duplicate address detection (DAD), SIP session reestablishment, and AAA procedure. This paper offers a fast handover solution for SIP-based mobility (named FMSIP for short). The proposed method utilizes movement anticipation, tunneling, and AAA context transfer to alleviate handover delay. The performance analysis and simulation results are presented at the end of this paper