Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

Characterizing multi‐hop localization for Internet of things

Deployments over large geographical areas in the Internet of Things (IoT) pose a major challenge for single‐hop localization techniques, giving rise to applications of multi‐hop localizations. And while many proposals have been made on implementations for multi‐hop localization, a close understanding of its characteristics is yet to be established. Such an understanding is necessary, and is inevitable in extending the reliability of location based services in IoT. In this paper, we study the characteristics of multi‐hop localization and propose a new solution to enhance the performance of multi‐hop localization techniques. We first examine popular assumptions made in simulating multi‐hop localization techniques, and offer rectifications facilitating more realistic simulation models. We identify the introduced errors to follow the Gaussian distribution, and the estimated distance follows the Rayleigh distribution. We next use our simulation model to characterize the effect of the number of hops on localization in both dense and sparse deployments. We find that, contrary to common belief, it is better to use long hops in sparse deployments, while short hops are better in dense deployments – despite the traffic overhead. Finally, we propose a new solution that decreases and manages the overhead generated during the localization process. Copyright © 2016 John Wiley & Sons, Ltd.     

Combined experimental and TDDFT computational studies of the optical and electrical characteristic of luminol films-doped TiO2 with 9.027% power conversion efficiency

Journal of Materials Science: Materials in Electronics - In this study, the spectrophotometric analysis was applied to check the optical properties of undoped [LO]TF and doped Luminol films with...     

Teaching single-cell digital analysis using droplet-based microfluidics

Microfluidics allows the manipulation of small quantities of reagents in a high-throughput manner and is therefore highly amenable to single cell characterization and more generally to digital analysis, with applications in fields as varied as genomics, diagnostics, directed evolution, and drug screening. The growing place of microfluidics in biology laboratories encouraged us to develop a teaching method where advanced undergraduate or first-year graduate-level students are taught to fabricate droplet-based microfluidic devices, characterize them, and finally use them to perform a digital analysis of bacterial samples based on a phenotypic marker.     

A critical review of asymptotic numerical methods

Summary Various sorts of asymptotic-numerical methods have been propsed in the literature: the reduced basis technique, direct computation of series or the use of Padé approximants. The efficiency of the method may also depend on the chosen path parameter, on the order of truncature and on alternative parameters. In this paper, we compare the three classes of asymptotic-numerical method, with a view to define the “best” numerical strategy.     

Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation

This paper discusses the accuracy performance of training, validation and prediction of monthly water quality parameters utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was used to analyse the historical data that were generated through continuous monitoring stations of water quality parameters (i.e. the dependent variable) of Johor River in order to imitate their secondary attribute (i.e. the independent variable). Nevertheless, the data arising from the monitoring stations and experiment might be polluted by noise signals owing to systematic and random errors. This noisy data often made the predicted task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this study was to develop a technique that could enhance the accuracy of water quality prediction (WQP). Therefore, this study proposed an augmented wavelet de-noising technique with Neuro-Fuzzy Inference System (WDT-ANFIS) based on the data fusion module for WQP. The efficiency of the modules was examined to predict critical parameters that were affected by the urbanization surrounding the river. The parameters were investigated in terms of the following: the electrical conductivity (COND), the total dissolved solids (TDSs) and turbidity (TURB). The results showed that the optimum level of accuracy was achieved by making the length of cross-validation equal one-fifth of the data records. Moreover, the WDT-ANFIS module outperformed the ANFIS module with significant improvement in predicting accuracy. This result indicated that the proposed approach was basically an attractive alternative, offering a relatively fast algorithm with good theoretical properties to de-noise and predict the water quality parameters. This new technique would be valuable to assist decision-makers in reporting the status of water quality, as well as investigating spatial and temporal changes.