An adaptive QoS supportive approach for user based services using Krill Herd Approach over Internet of Things (KHAI)

Author(s):  
V. Padmavathi ◽  
R. Saminathan ◽  
S. Selvamuthukumaran

The demand for a providing QoS adaptive routing over IoT networks is always a challenge among current research community. This research work KHAI proposes a framework for QoS-adaptive routing approach, which incorporates Krill Herd optimization model over IoT network. Variable QoS user preference and handling differential service types over a scalable IoT network shows that challenge for designing an adaptive QoS is a must. Research survey suggest that major works have been carried out on bandwidth appreciable services and route management approaches. Hence QoS adaptive user defined services, which adapt to variable service priority levels based on user demand and network resource utilization is proposed in this research work. The performance analysis of proposed approach shows an improved throughput of 97.51 Mbps and minimal packet loss of 37.29% over a session in comparison to traditional computational approaches. Considering large scale of interconnected IoT devices, proposed approach delivers near optimal solution of throughput and adaptive utilization of network resources.

Author(s):  
Haopeng Zhang ◽  
Qing Hui

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.


Application recommendation is one of the larger scales and sophisticated recommendation system currently exists. In this research work, we devised an approach which will deal with suggesting application based on users click on a particular application. The approach described in this paper is efficient and with less memory requirement than other traditional methods. This paper also includes the details about the implementation of the approach with User Interface. The paper also provides the details that how it can be implemented on a large scale. The approach is implemented in a mobile-based platform with react native support. The main objective of this paper is to describe an approach, which will be efficient and completely based on users data. The main objective of an Application Recommendation to recommend applications to increase user experience and recommend application based on their needs. Companies like Google, Apple, Samsung etc. are implementing it also.


Author(s):  
Fabian Lopez

Small geographic basic units (BU) are grouped into larger geographic territories on a Territory Design Problem (TDP). Proposed approach to solve a TDP is presented through a study case developed on a large soft drinks company which operates in the city of Monterrey, México. Each BU of our TDP is defined by three activity measures: (1) number of customers, (2) sales volume and (3) workload. Some geographic issues about contiguity and compactness for the territories to be constructed are considered. An optimal solution is obtained when the constructed territories are well balanced taking into consideration each activity measure simultaneously. In particular, contiguity is hard to be represented mathematically. All previous research work indicates that this NP-Hard problem is not suitable for solving on large-scale instances. A new strategy which is based on a hybrid-mixed integer programming (HMIP) approach is developed. Specifically, our implementation is based on a Cut-Generation Strategy. We take advantage from territory centers obtained through a relaxation of a P-median based model. This model has a very high degree of connectivity. Thus, small number of iterations to find connected solutions is required. The authors detail out their methodology and then they proceed to its computational implementation. Experimental results show the effectiveness of our method in finding near-optimal solutions for very large instances up to 10,000 BU’s in short computational times (less than 10 minutes). Nowadays, this model is being used by the firm with important economical benefits.


Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.


2019 ◽  
Author(s):  
Chem Int

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.


1990 ◽  
Vol 22 (3-4) ◽  
pp. 291-298
Author(s):  
Frits A. Fastenau ◽  
Jaap H. J. M. van der Graaf ◽  
Gerard Martijnse

More than 95 % of the total housing stock in the Netherlands is connected to central sewerage systems and in most cases the wastewater is treated biologically. As connection to central sewerage systems has reached its economic limits, interest in on-site treatment of the domestic wastewater of the remaining premises is increasing. A large scale research programme into on-site wastewater treatment up to population equivalents of 200 persons has therefore been initiated by the Dutch Ministry of Housing, Physical Planning and Environment. Intensive field-research work did establish that the technological features of most on-site biological treatment systems were satisfactory. A large scale implementation of these systems is however obstructed in different extents by problems of an organisational, financial and/or juridical nature and management difficulties. At present research is carried out to identify these bottlenecks and to analyse possible solutions. Some preliminary results are given which involve the following ‘bottlenecks':-legislation: absence of co-ordination and absence of a definition of ‘surface water';-absence of subsidies;-ownership: divisions in task-setting of Municipalities and Waterboards; divisions involved with cost-sharing;-inspection; operational control and maintenance; organisation of management;-discharge permits;-pollution levy;-sludge disposal. Final decisions and practical elaboration of policies towards on-site treatment will have to be formulated in a broad discussion with all the authorities and interest groups involved.


2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.


2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Arturo Basaure ◽  
Heikki Kokkinen ◽  
Heikki Hämmäinen ◽  
V. Sridhar

Radio spectrum for commercial mobile services continues to be scarce. Countries around the world have recognized the importance of efficient utilization of this scarce resource and have initiated regulatory and policy steps towards flexible approaches to spectrum management, including sharing of licensed spectrum, and releasing unlicensed spectrum for mobile services. Technologies for shared access and the associated standardization activities have also progressed towards possible large scale deployments. In this paper, we analyze the evolution of spectrum management policies using a causal model and indicate how the markets can lock in to either centralized or flexible approach. We also cite a use case of a flexible spectrum management approach using spectrum band fill option and indicate its suitability to the Indian context.


Author(s):  
Ruiyang Song ◽  
Kuang Xu

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.


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