Concurrent multipath transfer effectively improves network throughput and reliability by utilizing the unoccupied network links. However, the performance of concurrent multipath transfer degrades significantly in the heterogeneous Internet of Things. The reason is that the disorder and retransmission will increase sharply if the quality of links are greatly different. This paper proposes a multipath stateful forwarding mechanism in the granularity of flowlet, which is featured with planning the flowlet forwarding dynamically to improve the transmission success rate. Besides, our mechanism implements stateful forwarding by improving the state information processing capability of the data plane. It can adjust the size of the flowlet based on the network performance of the path, thereby improving the bandwidth resource utilization of the system. In particular, the proposed mechanism can sense network congestion status dynamically, detect network anomalies, and re-plan the forwarding strategy. Experimental results show that in the harsh environment (link quality varies greatly among the three test paths), the proposed mechanism can improve the throughput by 114.7% and reduce the percentage of out of order packets by 29.5% compared with the Round-Robin Scheduling mechanism.
The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.
Adaptive packet scheduling can efficiently enhance the performance of multipath Data Transmission. However, realizing precise packet scheduling is challenging due to the nature of high dynamics and unpredictability of network link states. To this end, this paper proposes a distributed asynchronous deep reinforcement learning framework to intensify the dynamics and prediction of adaptive packet scheduling. Our framework contains two parts: local asynchronous packet scheduling and distributed cooperative control center. In local asynchronous packet scheduling, an asynchronous prioritized replay double deep Q-learning packets scheduling algorithm is proposed for dynamic adaptive packet scheduling learning, which makes a combination of prioritized replay double deep Q-learning network (P-DDQN) to make the fitting analysis. In distributed cooperative control center, a distributed scheduling learning and neural fitting acceleration algorithm to adaptively update neural network parameters of P-DDQN for more precise packet scheduling. Experimental results show that our solution has a better performance than Random weight algorithm and Round-Robin algorithm in throughput and loss ratio. Further, our solution has 1.32 times and 1.54 times better than Random weight algorithm and Round-Robin algorithm on the stability of multipath data transmission, respectively.
Due to the severe environment along the High-Speed Railway (HSR), it is essential to research an efficient HSR communication system. In our previous work, we collected and analyzed an amount of the first hand dataset of signal intensity in HSR networks. We first observed that the link status variation presented an obvious Two-Time-Scale characteristics. However, that work did not analyze the cause of the Two-Time-Scale characteristics clearly. In this work, we focus on the fundamental cause of the periodic Two-Time-Scale characteristics, and make a lot of in-depth studies on this interesting phenomenon. Furthermore, we rebuild Two-Time-Scale characteristics by leveraging the relationship between the link state variation and the geographical position along HSR lines. In particular, considering the distribution of urban areas and rural ones along the HSR, a periodic distance based small time-scale model and a path-loss based large time-scale model are proposed respectively. Simulation results show the proposed models can perfectly explain the Two-Time-Scale characteristics and predict HSR link quality.
Multipath transmission is a critical enabling technology to enhance QoE for edge users. The packet scheduler plays an irreplaceable role in overcoming heterogeneity and dynamicity in multipath transmission. However, current schedulers depend on an inaccurate delay estimation and lack systematic traffic intensity awareness, performing poorly in wireless heterogeneous networks (HetNets). In this paper, we propose a novel traffic-aware two-level packet scheduler (TA2LS) to address the problem and improve aggregated bandwidth while trading off delay. In particular, we design a multipath transmission state machine (MTSM) to perceive link traffic intensity. MTSM replaces network prediction algorithms by identifying the contribution of each link in multipath transmission in a cost-effective way. Further, we propose a scheduling mechanism based on a two-level optimal-path evaluation method (2LOSM) to adjust the packet scheduling policy adaptively. 2LOSM increases the priority of links with low traffic intensity during scheduling, improving aggregated bandwidth performance and reducing end-to-end delay. We have built a real-world 4G/5G/WiFi testbed and deployed 47 dynamic scenarios to evaluate TA2LS and other five schedulers. In 4G/5G/WiFi scenarios, TA2LS improves aggregated bandwidth by 10.32%–48.27% compared to the second-best scheduler and reduces end-to-end delay by 5.04%–39.98% under the premise of fewer or equivalent overheads.
Concurrent multipath transfer (CMT) has greatly potential to significantly improve the end-to-end throughout with its multihoming property. However, due to the extremely high unpredictability of 6G heterogeneous networks, the receive buffer blocking problem seriously degrades the overall transmission reliability. To address this problem, this paper proposes a learning-based fountain codes for CMT (CMT-FC) scheme to mitigate the negative influence of the path diversity for 6G heterogeneous networks. Specifically, we first formulate a multidimensional optimal problem to mitigate receive buffer blocking phenomenon and improve the transmission rate with requirement constrains. Then, we transform the data scheduling and redundancy coding rate problem into a Markov decision process, and propose a deep reinforcement learning (DRL)-based fountain coding algorithm to dynamically adjust data scheduling policy and redundancy coding rate. Extensive experiments indicate the proposed algorithm mitigates the packet out-of-order problem, and improves the average throughput compared with traditional multipath transmission scheme.
We propose a graph-based RGBD image segmentation method that considers both depth and color information. Color and depth information are complementary to each other. However, compared with the RGB channels, the depth channel of an image has more noises and uncertainties that have negative effects to accurate segmentation. To partially solve this problem, we model the depth uncertainties of an image as a function of the distances and angles between the RGBD sensor and the observed surfaces. Then, the uncertainty model is applied to RGBD image segmentation in which the RGB and depth cues are combined according to the uncertainties of the depth measurements. The experimental results show that our method improves the segmentation accuracy.
The article provides the results of a set of analyses conducted to compare two major radio technologies, DECT/PWT-E and PACS, for their suitability in the local loop in the United States to provide voice and data services. DECT, digital enhanced cordless telecommunications, is a radio interface standard developed in Europe mainly for indoor wireless applications and being promoted lately for wireless local loop applications as well. PWT, personal wireless telecommunications, is a DECT-based standard developed by the TIA in the United States for the unlicensed PCS applications. PWT-E, enhanced, is the version that is suitable for the licensed PCS applications. PACS, personal access communications systems, is a total system standard (i.e. radio interface and associated network infrastructures) developed in the United States for licensed PCS applications. (PACS-UA and PACS-UB are the standards for the unlicensed PCS applications.) For the wireless local loop (WLL), we make an assumption that the radio technologies operating in the licensed PCS spectrum are more suitable to provide a quality of service that is expected traditionally from a local exchange company (LEC). Therefore, this article focuses on the PACS and PWT-E, rather than PACS-UB and PWT. Also note that the article focuses on the North American version, PWT-E, rather than the European version, DECT. It provides an introduction to the PACS and PWT technologies.
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