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.
With the capability to tune the carrier density of InZnO (IZO), a thin conducting IZO buffer at the channel/metallization interface is introduced to enhance the contact behaviors for amorphous IZO thin-film transistors (TFTs). Photoelectron spectroscopic measurements reveal that the conducting IZO buffer, of which the work function (Φ) is 4.37 eV, relaxes a relatively large Φ difference between channel IZO (Φ = 4.81 eV) and Ti (Φ = 4.2–4.3 eV) metallization. The buffer is found to lower the energy barrier for charge carriers at the source to reach the effective channel region near the dielectric. In addition, the higher carrier density of the buffer and favorable chemical compatibility with the channel (compositionally the same) further contribute to a significant reduction in specific contact resistance as much as more than 2.5 orders of magnitude. The improved contact and carrier supply performance from the source to the channel lead to an enhanced field effect mobility of up to 56.49 cm2/V s and a threshold voltage of 1.18 V, compared to 13.41 cm2/V s and 7.44 V of IZO TFTs without a buffer.
Transparent optoelectronic materials have gained significant attention for application in high performance devices such as next generation displays 1-2 , photovoltaics 3 , electrochromic devices 4 , and sensors 5-6 . Metal oxide-based semiconductors, specifically, are a promising group of transparent optoelectronic materials, now considered to be the vital building blocks for future device applications due to the unique combined properties of excellent optical transparency (from the visible to near-infrared regime), and high electrical conductivities 7-8 . Metal oxides have already seen wide implementation in various optoelectronic devices, depending on their electrical conductivity, as transparent electrodes by degenerately-doped oxides such as indium tin oxides (ITO) and doped zinc oxides (ZnO), as semiconducting active layers based on In 2 O 3 , ZnO or SnO 2 , and as insulators (e.g., SiO 2 , Al 2 O 3 , HfO 2 ) for dielectrics or encapsulations. The key pixel driving switches in display technologies, thin film transistors (TFTs), require a vast array of demanding material properties such as high carrier mobilities (usually defined as the “TFT field effect mobility”), low thermal budgets during processing, resilient phase stability, and reliable device performance under high thermal and bias stress conditions. Amorphous oxide semiconductors (AOSs), specifically those based on indium oxides, are receiving unique attention for TFT implementation due to their promisingly high carrier mobilities (> 5-20 cm 2 /Vs) compared to conventional amorphous Si (<~1 cm 2 /Vs), low processing temperature requirements (ambient to 200 °C), superior mechanical flexibility to their crystalline counterparts, and large area process-ability. Indium oxide-based binary and ternary cation material systems show tremendous promise for use in next generation displays as these materials exceed the aforementioned material property and fabrication requirements. Unfortunately, undoped In 2 O 3 experiences a rapid onset of microstructural crystallization at very low homologous temperatures ( T/T m <0.19 ) at 150 °C and struggles to maintain its amorphous phase structure. The inclusion of Zn in In 2 O 3 has revealed a viable and promising binary cation material which specifically addresses the structural instability of undoped In 2 O 3 , Indium Zinc Oxide (IZO), as the addition of Zn into In 2 O 3 proves to stabilize the temperature-sensitive amorphous phase of indium oxide. Furthermore, the reported carrier mobility of IZO has shown to be as high as 20-40 cm 2 /Vs for both Hall and 15-30 cm 2 /Vs for TFT field effect mobilities. Studies have dug deeper to further unveil the effects of doping of In 2 O 3 -basd materials, and the ternary cation system of indium gallium zinc oxides (IGZO) has proven to be even more popular than binary systems as the addition of Ga in IGZO allows for the controllable suppression of channel carrier density during TFT applications (preferred for TFT devices where a low device off-state current is desired). In addition to Ga, more third cation species have been investigated as suitable material candidates such as Hf, Si, and Zr, but the carrier mobilities (~3-10 cm 2 /Vs) are around 3-10 times lower than that of In 2 O 3 or IZO. Therefore, securing strategies to develop a material system which maintains both high carrier mobility (e.g., >~20 cm 2 /Vs) and suppresses carrier generation for TFT channel application is of significant importance, and is necessary to expedite the realization of next-generation transparent displays which possess reliable performance, fast switching speed, and, consequently, ultra-high definition resolution. In this study, the ternary cation oxide system of indium aluminum zinc oxide (IAZO) is investigated with varying Al concentration in IAZO thin films. The IAZO thin films were deposited using magnetron co-sputtering at room temperature. The structural, optical, and electrical properties were systematically characterized as a function of Al concentration and compared to baseline IZO samples. The carrier transport characteristics, as well as the dominant mechanisms for carrier density and resistivity and their relation to Al concentration, are discussed. Furthermore, amorphous IAZO-based TFTs were developed to objectively compare and validate device performance and parameters against IZO-based TFTs. The authors gratefully acknowledge the financial supports of the U.S. NSF Award No. ECCS-1931088; the Purdue Research Foundation (Grant No. 60000029); and the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 20011028) by KRISS. References Nomura et al. Nature 2004, 432 (7016), 488-492. Lee et al. Applied Physics Letters 2014, 104 (25), 252103. Fortunato et al. MRS Bull. 2007, 32 (3), 242-247. Maho et al. Journal of The Electrochemical Society 2017, 164 , H25-H31. Venkatanarayanan et al. Elsevier: Oxford, 2014; pp 47-101. Zhao et al. The Journal of Physical Chemistry C 2015, 119 (26), 14483-14489. Lewis et al. MRS Bull. 2000, 25 (8), 22-27. Coutts et al. MRS Bull. 2000, 25 (8), 58-65. Figure 1
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.
With the continuous development of autonomous vehicles, telemedicine, digital media and other time-sensitive applications, a soaring number of network services have high demand for the quality of service (QoS) with extra low delay and jitter. Traditional network architecture only offers best-effort services which cannot meet the stringent delay and jitter requirements. In this paper, we propose a resilient-priority queue scheduling algorithm (RPQ) for delay-sensitive services. RPQ can guarantee stable delay in a fine-grained manner. Particularly, on the premise of meeting the delay requirements of high priority streams, RPQ can give consideration to the delay requirements of lower priority streams depending on its resilient scheduling mechanism. We implement RPQ on programmable switch. The experimental results show that RPQ not only guarantees QoS with low delay and low jitter for delay-sensitive streams but also improves network throughput by comparing with the existing solutions, i.e., SP-PIFO and WRR.
The initial development of upward positive and negative leader is studied and compared based on the simultaneous data from high-speed video camera, channel base current, and electromagnetic field changes documented in the rocket-triggered lightning and tower-initiated lightning. The impulsive current, electric field and corresponding optical images suggest stepping-like propagation of positive leader at the initial stage. However, the individual positive leader step, if any, is weak in terms of peak current, charge transfer and step length than the negative one. The negative charge of an individual step was nearly an order of magnitude larger than that of the positive leader step in triggered lightning. The intermittent brush-like corona zone and the intermittent re-illumination of the channel body are the most distinct features for the positive leader stepping, which is obviously different from the stepping processes of the negative leader which is characterized by space leader/stem.
The negative thermo-optic properties of TiO2 have been considered promising for athermal photonic devices that can mitigate the optical-performance instability due to temperature variations. When temperature increases, its negative thermo-optic coefficient (TOC) can compensate for the unfavorable increase in refractive index exhibited by widely used optical materials such as Si, which have positive TOCs. Herein, the structure–property relationship of TiO2 is thoroughly investigated to understand the negative thermo-optic behaviors of TiO2. Through atomic layer deposition and mild thermal annealing, the obtained negative TOC values are as high as −2.30 × 10–4/°C in the visible to the near-infrared regime. X-ray diffraction/reflectivity and temperature-dependent refractive index measurements identify that the higher crystallinity of anatase TiO2 leads to greater negative TOC values due to its higher density and lower porosity, compared to those of amorphous or weakly crystalline states. The Prod'homme model and band gap analysis reveal that the effect of volume expansion is more dominant on the enhanced negative TOC of TiO2, rather than the polarizability. Photoelectron spectroscopy measurements suggest an amorphous relaxation process during annealing that further supports the amorphous-to-crystalline transformation in TiO2. The findings of the structure and chemical properties governing the negative TOC of TiO2 for athermal applications may be of significant relevance to many photonic devices showing strong performance instability due to the high positive TOCs.
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.
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