Geographical forwarding algorithm based video content delivery scheme for internet of vehicles (IoV)

This is an accepted manuscript of an article published by IEEE Multimedia Communications Technical Committee in MMTC Communications – Frontiers on 31/07/2020, available online: https://mmc.committees.comsoc.org/files/2020/07/MMTC_Communication_Frontier_July_2020.pdf The accepted version of the publication may differ from the final published version.An evolved form of Vehicular Ad hoc Networks (VANET) has recently emerged as the Internet of Vehicles (IoV). Though, there are still some challenges that need to be addressed in support IoV applications. The objective of this research is to achieve an efficient video content transmission over vehicular networks. We propose a balanced video-forwarding algorithm for delivering video-based content delivery scheme. The available neighboring vehicles will be ranked to the vehicle in forwarding progress before transmitting the video frames using proposed multi-score function. Considering the current beacon reception rate, forwarding progress and direction to destination, in addition to residual buffer length; the proposed algorithm can elect the best candidate to forward the video frames to the next highest ranked vehicles in a balanced way taking in account their residual buffer lengths. To facilitate the proposed video content delivery scheme, an approach of H.264/SVC was improvised to divide video packets into various segments, to be delivered into three defined groups. These created segments can be encoded and decoded independently and integrated back to produce the original packet sent by source vehicle. Simulation results demonstrate the efficiency of our proposed algorithm in improving the perceived video quality compared with other approache

Smart routing: towards proactive fault handling of software-defined networks

In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic in both the industrial and academic sectors. Software-defined networking offers numerous benefits against legacy networking systems by simplifying the process of network management through reducing the cost of network configurations. Currently, data plane fault management is limited to two mechanisms: proactive and reactive. These fault management and recovery techniques are activated only after a failure occurrence and hence packet loss is highly likely to occur. This is due to convergence time where new network paths will need to be allocated in order to forward the affected traffic rather than drop it. Such convergence leads to temporary service disruption and unavailability. Practically, not only the speed of recovery mechanisms affects the convergence, but also the delay caused by the process of failure detection. In this paper, we define a new approach for data plane fault management in software-defined networks where the goal is to eliminate the convergence process altogether rather than accelerate the failure detection and recovery. We propose a new framework, called Smart Routing, which allows the network controller to receive forewarning signs on failures and hence avoid risky paths before the failure incidents occur. The proposed approach aims to decrease service disruption, which in turn increases network service availability. We validate our framework through a set of experiments that demonstrate how the underlying model runs and its impact on improving service availability. We take as example of the applicability of the new framework three types of topologies covering real and simulated networks

A Dynamic and Adaptive Transmission Scheme for Both Solving Uplink/Downlink Unfairness and Performance Anomaly Problems in a Multi-Rate WLAN

Abstract: Uplink/downlink fairness and performance efficiency are both considerable issues in an IEEE 802.11 multi-rate Wireless Local Area Network (WLAN). The IEEE 802.11 Distributed Coordination Function (DCF) provides equal medium access probability to all transmitters that cause the access point (AP) to obtain less bandwidth than that of the wireless mobile stations to download traffic when the number of mobile stations is larger than one. Furthermore, the WLAN with infrastructure mode also has the performance anomaly problem that the system throughput was seriously degraded by the transmissions of lower date rate transmitters in a multi-rate environment. In the past studies, many mechanisms have been proposed to solve the uplink/downlink unfairness problem, such as the transmission opportunity mechanism (TXOP), the multiple backoff timer mechanism (MBT) and the asymmetric access point mechanism (AAP). In order to improve the performance efficiency, contention window differentiation mechanism (CWD), packet size differentiation mechanism (PSD) and interframe gap differentiation mechanism (IFG) have been proposed recently. The proposed mechanisms, however, did not take both uplink/downlink unfairness and performance anomaly problems into consideration at the same time. In fact, the two problems occur simultaneously in practical WLAN environments. In this paper, we propose a dynamic and adaptive transmission scheme (DAT) to deal with the both problems. Each wireless mobile station will consider its data rate to decide the number of packets to transmit when it gets the privilege to access medium. Moreover, the AP has more right to download more packets for the purpose of balancing total uplink traffic. The system throughput of the proposed DAT is discussed and validated by the simulations and analytical results. The simulations also show that the proposed DAT outperforms the previous mechanisms

Bayesian Adaptive Path Allocation Techniques for Intra-Datacenter Workloads

Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its allocation procedure, and updates this information on-the-fly during DCN operational time. We evaluate the proposed framework using an experimental platform built with the POX controller and the Mininet emulator. Compared with a benchmark shortest path algorithm, the results show that the proposed methods perform better in terms of reducing the packet loss and routing flaps

Frame-based mapping mechanism for energy-efficient MPEG-4 video transmission over IEEE 802.11e networks with better quality of delivery

Recent developments in hardware, software and communication technologies have resulted in increasing interest in the use of wireless local area networks (WLANs). Mobile devices with embedded WLAN functionality are becoming increasingly popular. Such devices must be designed to support applications that require high quality of service (QoS) and have favorable to maximize battery capacity. The resources of queues in IEEE 802.11e networks may be wasted by the transmission of information that is useless to the receiver. This work develops a frame-based mapping mechanism (FBM) that exploits different methods to process I/P/B (Intra/Predictive/Bipredictive) video frame packets. FBM refers to the dropping of arriving packets if the preceding packets in the same video frame have been dropped. When fragmented packets of a single frame are allocated to different access categories (AC) queues, out-of order delivery may occur. Hence, FBM tries to treat all fragmented packets of each video frame equally and allocates them to the same AC queue if possible. The simulation results demonstrate that transmission by the FBM is more efficient than that by other mechanisms, such as EDCA (Enhanced Distributed Channel Access), static mapping and adaptive mapping, suggesting that the energy of a device is not wasted in the transmission of useless video data in WLANs. (C) 2015 Elsevier Ltd. All rights reserved.Foundation item: The National Project of Taiwan (No.: MOST 103-2221-E507-001). Authors are grateful to Ministry of Science and Technology Grant no. (MOST 103-2221-E507-001), Government of Taiwan for financial support to carry out this work.Ke, C.; Yang, C.; Chen, J.; Ghafoor, KZ.; Lloret, J. (2015). Frame-based mapping mechanism for energy-efficient MPEG-4 video transmission over IEEE 802.11e networks with better quality of delivery. Journal of Network and Computer Applications. 58:280-286. https://doi.org/10.1016/j.jnca.2015.08.005S2802865

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data