User relay assisted traffic shifting in LTE-advanced systems

In order to deal with uneven load distribution, mobility load balancing adjusts the handover region to shift edge users from a hot-spot cell to the less-loaded neighbouring cells. However, shifted users suffer the reduced signal power from neighbouring cells, which may result in link quality degradation. This paper employs a user relaying model and proposes a user relay assisted traffic shifting (URTS) scheme to deal with the above problem. In URTS, a shifted user selects a suitable non-active user as relay user to forward data, thus enhancing the link quality of the shifted user. Since the user relaying model consumes relay user’s energy, a utility function is designed in relay selection to reach a trade-off between the shifted user’s link quality improvement and the relay user’s energy consumption. Simulation results show that URTS scheme could improve SINR and throughput of shifted users. Also, URTS scheme keeps the cost of relay user’s energy consumption at an acceptable level

A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding

This paper studies and models the multicast routing problem with network coding in dynamic network environment, where computational and bandwidth resources are to be jointly optimized. A quantum inspired evolutionary algorithm (QEA) is developed to address the problem above, where a restart scheme is devised for well adapting QEA for tracing the ever-changing optima in dynamic environment. Experimental results show that the proposed QEA outperforms a number of existing evolutionary algorithms in terms of the best solution obtained

MetaLoc: Learning to Learn Wireless Localization

The existing indoor fingerprinting-based localization methods are rather accurate after intensive offline calibrations for a specific environment, and they are built based either on the received signal strength (RSS) or the channel state information (CSI). However, a well-calibrated localization method (which can be a pure statistical signal processing method or an emerging data-driven method) will present poor generalization abilities in changing environments, which results in large losses in knowledge and human effort. To break the environment-specific localization bottleneck, we propose a novel data-driven fingerprinting-based localization framework empowered by the model-agnostic meta-learning (MAML), named MetaLoc. Specifically, MetaLoc is characterized by its ability to rapidly adapt itself to a new, possibly unseen, environment with very little calibration. The underlying data-driven localization model is a deep neural network, and we leverage historical data previously collected from various well-calibrated environments to train an optimal set of meta-parameters as an initialization to the new environments. Furthermore, we develop two MetaLoc paradigms in the proposed MetaLoc based on the different ways of obtaining meta-parameters. The centralized paradigm using vanilla MAML is much easier to implement, while the distributed paradigm incorporates domain shifts into the vanilla MAML to accelerate the convergence speed of the training process. The experimental results obtained for both synthetic- and real datasets demonstrate MetaLoc's strengthes in terms of localization error, robustness and cost-effectiveness compared with various baseline methods

Towards efficient battery swapping service operation under battery heterogeneity

The proliferation of electric vehicles (EVs) has posed significant challenges to the existing power grid infrastructure. It thus becomes of vital importance to efficiently manage the Electro-Mobility for large demand from EVs. Due to limited cruising range of EVs, vehicles have to make frequent stops for recharging, while long charging period is one major concern under plug-in charging. We herein leverage battery swapping (BS) technology to provide an alternative charging service, which substantially reduces the charging duration (from hours down to minutes). Concerning in practice that various battery is generally not compatible with each other, we thus introduce battery heterogeneity into the swapping service, concerning the case that different types of EVs co-exist. A battery heterogeneity-based swapping service framework is then proposed. Further with reservations for swapping service enabled, the demand load can be anticipated at BS stations as a guidance to alleviate service congestion. Therefore, potential hotspots can be avoided. Results show the performance gains under the proposed scheme by comparing to other benchmarks, in terms of service waiting time, etc. In particular, the diversity of battery stock across the network can be effectively managed

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

Pulsatility Index as a Novel Parameter for Perfusion in Mouse Model of Hindlimb Ischemia

Background/Aims: In clinical settings, the pulsatility index (PI) has become a widely used tool for monitoring obstetrics or other vascular diseases. It is based on the maximum Doppler shift waveform derived from ultrasonography. However, it remains unclear whether the PI levels are correctly predicted from the perfusion in mouse model of hindlimb ischemia. Methods: To explore the relationship between PI and perfusion, we generated a unilateral hindlimb ischemia model in 8-week-old C57BL/6 male mice by ligation of the right common iliac artery and femoral artery. These mice were monitored with laser Doppler perfusion imaging (LDPI) and an ultrasound system (Vevo2100). Vessel densities in ischemic skeletal muscles were measured with vWF staining, which functions as a marker for endothelial cells. In order to further verify PI evaluation in other conditions, we performed therapeutic experiments using hindlimb ischemic mouse with PBS or FGF2 treatment. Results: In the mouse model of hindlimb ischemia, the PI levels were continuously elevated and were accompanied by an increased ratio of perfusion to blood flow. 1 and 4 weeks after ischemia, the densities of vWF staining were correlated with PI values. Moreover, the PI index exactly reflected the perfusion in hindlimb ischemic mice after FGF2 treatment, while it indicated the condition of angiogenesis after therapeutic treatment based on the association between PI values and the number of vWF-positive stained cells in muscles. Conclusion: This study confirms the utility of a noninvasive and reproducible ultrasound index for a rapid evaluation of perfusion and blood recovery after hindlimb ischemia in vivo. PI, as one stable and comparable parameter, is correlated with angiogenesis in hindlimb ischemic mouse. Moreover, PI can exactly reflect perfusion and angiogenesis in therapeutic hindlimb ischemic mouse models. This study suggested that PI can serve as a novel index for relatively reproducible and repeatable blood flow recovery in the evaluation of emerging ischemic therapies and disease development in mouse models of hindlimb ischemia

In Silico Screening and Evolution of Promising Novel Anti-Virulents Against Salmonella ATPase

Our current treatments for bacterial infections are under threat by the growth of antibiotic resistance in many different pathogens. Of these pathogens, Salmonella is a particularly widespread microbe, infecting over a million people annually as the leading source of food-borne diseases. One potential solution for antibiotic-resistant Salmonella is virulence inhibition of the bacteria’s T3SS injection system, which has been shown to destroy Salmonella’s proliferative abilities. Here, we identify fourteen compounds, primarily novel ligands, that exhibit high in-vitro potential as Salmonella inhibitors by attacking the ATPase InvC protein vital for T3SS injection–an enzyme that has not been previously evaluated for small-molecule inhibition. We also present a statistical analysis of AutoGrow4, a virtual structure-based molecular design tool that evolves ligands to better suit a target protein using Autodock Vina binding affinity calculations. Together, these create an entirely open-source workflow towards computational identification and evaluation of novel chemical treatments