Analysis of energy-tax for multipath routing in wireless sensor networks

Recently, multipath routing in wireless sensor networks (WSN) has got immense research interest due to its capability of providing increased robustness, reliability, throughput, and security. However, a theoretical analysis on the energy consumption behavior of multipath routing has not yet been studied. In this paper, we present a general framework for analyzing the energy consumption overhead (i.e., energy tax) resulting from multipath routing protocol in WSN. The framework includes a baseline routing model, a network model, and two energy consumption schemes for sensor nodes, namely, periodic listening and selective wake-up schemes. It exploits the influence of node density, link failure rates, number of multiple paths, and transmission environment on the energy consumption. Scaling laws of energy-tax due to routing and data traffic are derived through analysis, which provide energy profiles of single-path and multipath routing and serve as a guideline for designing energy-efficient protocols for WSN. The crossover points of relative energy taxes, paid by single-path and multipath routing, reception, and transmission, are obtained. Finally, the scaling laws are validated and performance comparisons are depicted for a reference network via numerical results.     

Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM

Multimedia Tools and Applications - The natural population-based prediction of type 2 diabetes is costly since it needs a high number of resources. Even though much research has used machine...     

Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

Engineering with Computers - Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning...     

A Mechanism for Carbon Depletion at Bondline of High-Frequency Electric-Resistance-Welded X70 Pipeline Steel

Metallurgical and Materials Transactions A - The bondline of electric-resistance-welded (ERW) linepipe steel, often etched white (i.e., ferrite) in optical microscopy, is generally believed to be...     

Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be intercepted through two effective techniques: Cloudflare and K-nearest neighbors (KNN) classification. Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks. However, the KNN classifiers will determine which area the spammer belongs to. At the end of the article, various prevention techniques for securing cloud servers will be discussed, a comparison will be made with different papers, a conclusion will be drawn based on different results.     

UKI-03 device for checking the generator water-cooled stator winding insulation

Conclusion A diagnostic microprocessor device for checking the generator water-cooled stator winding insulation, realizing semiautomatic measuring of the level of resistance and absorption coefficient of the stator winding insulation, in which case the shunting effect of the water hoses is eliminated, was developed and is operating successfully. Translated from Gidrotekhnicheskoe Stroitel'stvo, No. 3, pp. 31–33, March, 1996.     

Estimating WCET using prediction models to compute fitness function of a genetic algorithm

Real-Time Systems - Genetic algorithms can be used to generate input data in a real-time system that produce the worst-case execution time of a task. While generating the test data, the fitness...     

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

     

Interventions for long-term software security: Creating a lightweight program of assurance techniques for developers

Though some software development teams are highly effective at delivering security, others either do not care or do not have access to security experts to teach them how. Unfortunately, these latter teams are still responsible for the security of the systems they build: systems that are ever more important to ever more people. We propose that a series of lightweight interventions, six hours of facilitated workshops delivered over three months, can improve a team's motivation to consider security and awareness of assurance techniques, changing its security culture even when no security experts are involved. The interventions were developed after an Appreciative Inquiry and Grounded Theory survey of security professionals to find out what approaches work best. We tested the interventions in a participatory action research field study where we delivered the workshops to three software development organizations and evaluated their effectiveness through interviews beforehand, immediately afterwards, and after twelve months. We found that the interventions can be effective with teams with limited or no security experience and that improvement is long-lasting. This approach and the learning points arising from the work here have the potential to be applied in many development teams, improving the security of software worldwide.     

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Neural Computing and Applications - Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research,...