CH Selection via Adaptive Threshold Design Aligned on Network Energy

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (σ1) to mix design parameters and principal stresses (σ2,σ3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions

Residual energy-based cluster-head selection in WSNs for IoT application

© 2014 IEEE. Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network.

BACKGROUND:The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods. METHODS:In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN. RESULTS:These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification. CONCLUSION:The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis. SIGNIFICANCE:Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension

SDCF: A Software-Defined Cyber Foraging Framework for Cloudlet Environment

© 2004-2012 IEEE. The cloudlets can be deployed over mobile devices or even fixed state powerful servers that can provide services to its users in physical proximity. Executing workloads on cloudlets involves challenges centering on limited computing resources. Executing Virtual Machine (VM) based workloads for cloudlets does not scale due to the high computational demands of a VM. Another approach is to execute container-based workloads on cloudlets. However, container-based methods suffer from the cold-start problem, making it unfit for mobile edge computing scenarios. In this work, we introduce executing serverless functions on Web-assembly as workloads for both mobile and fixed state cloudlets. To execute the serverless workload on mobile cloudlets, we built a lightweight Web-assembly runtime. The orchestration of workloads and management of cloudlets or serverless runtime is done by introducing software-defined Cyber Foraging (SDCF) framework, which is a hybrid controller including a control plane for local networks and cloudlets. The SDCF framework integrates the management of cloudlets by utilizing the control plane traffic of the underlying network and thus avoids the extra overhead of cloudlet control plane traffic management. We evaluate SDCF using three use cases: (1) Price aware resource allocation (2) Energy aware resource scheduling for mobile cloudlets (3) Mobility pattern aware resource scheduling in mobile cloudlets. Through the virtualization of cloudlet resources, SDCF preserves minimal maintenance property by providing a centralized approach for configuring and management of cloudlets

I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring

© 2014 IEEE. Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput

Analysis of slopes using elitist differential evolution algorithm

Stability analyses of slopes have been a challenge for engineers, requiring development of complex numerical models to assess the risk levels and potential hazards. The numerical models involve combination of analysis methods and integrated optimization approaches, which generally induce intense engineering calculations with upscale time complexity. To obtain good and quick solutions, a robust optimization algorithm is necessary, leading to an efficient and reliable stability analysis framework. Within this context, various optimization techniques involving deterministic and metaheuristic approaches were proposed in the past decades. The proposed methods often suffer from convergence issues have time deficiencies, which highlights a necessity of development of an effective optimization algorithm. In this study, a modified version of Differential Evolution (DE) algorithm named Elitist Differential Evolution (EDE) is proposed to solve slope stability analysis problems. To develop a complete analysis framework, EDE is integrated with a non-circular failure surface generation method and limit equilibrium based stability analysis techniques. Its performance is compared with other optimization algorithms such as conventional DE, Particle Swarm Optimization and Grey Wolf Optimizer using benchmark problems reported in the literature. The experiments demonstrate that EDE greatly improves the results of other alternatives, validating the applicability of the algorithm to slope stability analysis. Furthermore, statistical performance of EDE has become prominent in the experiments, which further emphasizes its robustness