Grouping-Enabled and Privacy-Enhancing Communications Schemes for VANETs

Open Access Bookpublished_or_final_versio

Pricing Link by Time

The combination of loss-based TCP and drop-tail routers often results in full buffers, creating large queueing delays. The challenge with parameter tuning and the drastic consequence of improper tuning have discouraged network administrators from enabling AQM even when routers support it. To address this problem, we propose a novel design principle for AQM, called the pricing-link-by-time (PLT) principle. PLT increases the link price as the backlog stays above a threshold β, and resets the price once the backlog goes below β. We prove that such a system exhibits cyclic behavior that is robust against changes in network environment and protocol parameters. While β approximately controls the level of backlog, the backlog dynamics are invariant for β across a wide range of values. Therefore, β can be chosen to reduce delay without undermining system performance. We validate these analytical results using packet-level simulation

Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet

Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain Monte Carlo (MCMC) sampling to generate lexically constrained sentences, but they randomly determined the position to be edited and the action to be taken, resulting in many invalid refinements. To overcome this challenge, we used a classifier to instruct the MCMC-based models where and how to refine the candidate sentences. First, we developed two methods to create synthetic data on which the pre-trained model is fine-tuned to obtain a reliable classifier. Next, we proposed a two-step approach, “Predict and Revise”, for constrained sentence generation. During the predict step, we leveraged the classifier to compute the learned prior for the candidate sentence. During the revise step, we resorted to MCMC sampling to revise the candidate sentence by conducting a sampled action at a sampled position drawn from the learned prior. We compared our proposed models with many strong baselines on two tasks, generating sentences with lexical constraints and text infilling. Experimental results have demonstrated that our proposed model performs much better than the previous work in terms of sentence fluency and diversity. Our code, pre-trained models and Appendix are available at https://github.com/NLPCode/MCMCXLNet

Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance

Lexically constrained neural machine translation (NMT), which leverages pre-specified translation to constrain NMT, has practical significance in interactive translation and NMT domain adaption. Previous work either modify the decoding algorithm or train the model on augmented dataset. These methods suffer from either high computational overheads or low copying success rates. In this paper, we investigate Att-Input and Att-Output, two alignment-based constrained decoding methods. These two methods revise the target tokens during decoding based on word alignments derived from encoder-decoder attention weights. Our study shows that Att-Input translates better while Att-Output is more computationally efficient. Capitalizing on both strengths, we further propose EAM-Output by introducing an explicit alignment module (EAM) to a pretrained Transformer. It decodes similarly as EAM-Output, except using alignments derived from the EAM. We leverage the word alignments induced from Att-Input as labels and train the EAM while keeping the parameters of the Transformer frozen. Experiments on WMT16 De-En and WMT16 Ro-En show the effectiveness of our approaches on constrained NMT. In particular, the proposed EAM-Output method consistently outperforms previous approaches in translation quality, with light computational overheads over unconstrained baseline

Statistical Connection AdmissionControl Framework Based on Achievable Capacity Estimation

Traditional traffic descriptor-based and measurement-based admission control schemes are typically combined with a node by node resource reservation scheme, rendering them unscalable. Although some Endpoint Admission Control schemes can resolve this problem, they impose significant signaling overhead. To cope with these two problems, this paper proposes a statistical connection admission control framework which can easily and efficiently estimate the network resource for a pair of ingress-egress nodes and make admission decision based on this estimated result. In this framework, the network is considered as a "black box." For a certain ingress-egress node pair, the egress node measures the QoS constraint violation ratio and feeds this information back to the ingress node periodically. With this information and the measured statistical characteristics of the existing aggregated traffic, the ingress node estimates the achievable capacity between the ingress-egress node pair, and makes the admission decision for a new traffic connection request. The signaling overhead of this framework is very small. Simulation results show the effective throughput is relatively high

A Stability-Based Link State Updating Mechanism for QoS Routing Performance

QoS routing. which satisfies diverse application requirements and optimizes network resource utilization needs accurate link states to compute paths. Suitable link state update. (LSU) algorithms which ensure timely propagation of link state, information are thus critical. Since traffic fluctuation is one of the key reasons for link state uncertainty and the existing approaches can not effectively describe its statistical characteristics, in this paper, we propose a novel stability-based (SB) LSU-mechanism which consists of a second-moment-based triggering. policy and a corresponding stability-based routing algorithm. They incorporate knowledge of link state stability in computing a stability measure for link metrics. With extensive simulations, we investigate the performance of SB LSU mechanism and evaluate. its effectiveness compared with existing approaches. Simulation results show that SB LSU can achieve good performance in terms of traffic rejection ratio, successful transmission ratio, efficient throughput and link state stability. while maintaining a moderate. volume of update traffic

Contention-Based Prioritized Opportunistic Medium Access Control in Wireless LANs

In wireless environments, the inherent time-varying characteristics of the channel pose great challenges on medium access control design. In recent years, multiuser diversity and opportunistic medium access control schemes have been proposed to deal with the channel variation in order to efficiently improve the network throughput. In this paper, we propose a novel MAC protocol called Contention-Based Prioritized Opportunistic (CBPO) Medium Access Control Protocol. This protocol takes advantage of multiuser diversity, rate adaptation, which utilizes the multi-rate capability offered by IEEE 802.11, and black-burst (BB) contention to access the shared medium in a distributed manner. In particular, rather than simply measuring the channel condition for a node pair in communications each time, with the help of multicast RTS, the candidate users with qualified channel condition are selected and prioritized. Then the qualified receivers contend to send back prioritized clear-to-send message (CTS) with BB, which is a pulse of energy, the duration of which is proportional to the CTS priority. The user with the best channel quality is always selected to send back CTS and receive packets from the sender. Extensive simulation results show that our protocol achieves much better performance than IEEE 802.11 and other auto rate schemes with minimal additional overhead