A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks

Reliable routing of packets in a Mobile Ad Hoc Network (MANET) has always been a major concern. The open medium and the susceptibility of the nodes of being fault-prone make the design of protocols for these networks a challenging task. The faults in these networks, which occur either due to the failure of nodes or due to reorganization, can eventuate to packet loss. Such losses degrade the performance of the routing protocols running on them. In this paper, we propose a routing algorithm, named as learning automata based fault-tolerant routing algorithm (LAFTRA), which is capable of routing in the presence of faulty nodes in MANETs using multipath routing. We have used the theory of Learning Automata (LA) for optimizing the selection of paths, reducing the overhead in the network, and for learning about the faulty nodes present in the network. The proposed algorithm can be juxtaposed to any existing routing protocol in a MANET. The results of simulation of our protocol using network simulator 2 (ns-2) shows the increase in packet delivery ratio and decrease in overhead compared to the existing protocols. The proposed protocol gains an edge over FTAR, E2FT by nearly 2% and by more than 10% when compared with AODV in terms of packet delivery ratio with nearly 30% faulty nodes in the network. The overhead generated by our protocol is lesser by 1% as compared to FTAR and by nearly 17% as compared to E2FT when there are nearly 30% faulty nodes.     

Impact of Annual Average Daily Traffic on Highway Runoff Pollutant Concentrations

The objective of this study was to evaluate correlations between annual average daily traffic (AADT) and storm water runoff pollutant concentrations generated from California Department of Transportation (Caltrans) highway sites. Analyses of data collected from the Caltrans four-year (1997–2001) highway runoff characterization program revealed that, in general, pollutant concentrations from urban highways were higher than those found from nonurban highways. For a limited number of pollutants, however, the concentrations from nonurban highways were found to be higher than the concentrations from urban highways. No direct linear correlation was found between highway runoff pollutant event mean concentrations and AADT. However, through multiple regression analyses, it was shown that AADT has an influence on most highway runoff constituent concentrations, in conjunction with factors associated with watershed characteristics and pollutant build-up and wash off. The other noticeable factors shown to influence the accumulation of pollutants on highways were antecedent dry period, drainage area, maximum rain intensity, and land use.     

Energy harvesting-enabled energy-efficient routing using spotted hyena optimization in wireless sensor network

With the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)-enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH-enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH-enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH-enabled energy-efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster-based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH-enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques.     

Potential role of power authorities in offshore wind power development in the US

This article examines how power authorities could facilitate and manage offshore wind power development in US coastal waters. The power authority structure is an American 20th century institution for managing energy resources—a form of a public authority or public corporation dedicated to creating, operating and maintaining electric generation and transmission infrastructure. Offshore wind power is characterized by high capital costs but no fuel costs and thus low operating costs. Therefore a power authority, by virtue of its access to low-cost capital and managerial flexibility, could facilitate offshore wind power development by reducing financial risk of developing and lowering debt payments, thus improving the risk profile and lowering the cost of electricity production. Additionally, power authorities can be made up of multiple states, thus opening the possibility for joint action by neighboring coastal states. Using primary and secondary data, we undertake an in-depth analysis of the potential benefits and shortcomings of a power authority approach.     

What about Privacy of My OSN Data?

In the arena of internet of things, everyone has the ability to share every aspect of their lives with other people. Social media is the most popular and effective medium to provide communication. Social media has gripped our lives in a dramatic way. Privacy of users data lying with the service providers needs to be preserved when published for the purpose of research as the release of sensitive personal information of an individual may pose security threats. This has become an important research area nowadays. To some extent, the concepts of anonymization that were earlier used to preserve privacy of relational microdata have been applied to preserve privacy of social networks data. Anonymizing social networks data is challenging as it is a complex structure with users connected to one another graphically and the most important is to preserve the structural properties of the graph depicting the social network relationships while applying such concepts. Recent studies based upon K-anonymity and L-diversity help to preserve privacy of online social networks data and subsequently identify attacks that arise while applying these techniques in different scenarios. K-anonymity equalizes the degree of the nodes to prevent the data from identity disclosure but it cannot preserve sensitive information and also cannot handle attacks arising due to background knowledge and homogeneity. To cope up with the drawbacks of K anonymity, L-diversity was introduced that protects the sensitive labels of the users. In this paper, a novel technique has been proposed which implements the combined features of K-anonymity and L-diversity. Our proposed approach has been validated using the data of real time social network–Twitter (most popular microblogging network). The performance of the proposed technique has been measured by the metrics, such as average path length, average change in sensitive labels, and remaining ratio of top influential users. It thus becomes evident from the results that the values of these parameters attained with the proposed technique for the anonymized graph has minimal variation to that of original structural graph. So, it is possible to retain the utility without compromising privacy while publishing social networks data. Further, the performance of the proposed technique has been discussed by calculating the information loss that addresses the concern of preserving privacy with the least variation of actual content viz info loss.     

Characterization of Vacuum Plasma Sprayed Reinforced Hydroxyapatite Coatings on Ti–6Al–4V alloy

Two reinforced hydroxyapatite (HA) coatings with an intermediate layer of zirconia were deposited on Ti–6Al–4V by vacuum plasma spray (VPS) technique. In first coating, HA was reinforced with 10 wt % Al₂O₃ whereas in second coating, HA was reinforced with 10 wt % ZrO₂. The objective of this study was to investigate the microstructure, phase formation and mechanical properties like hardness and bond strength of as-sprayed coatings and the coatings after post coating heat treatment at 700 °C for 1 h. The characterization of the coatings was performed by using SEM/EDAX, XRD, porosity, crystallinity and roughness measurement. The coatings were also evaluated for mechanical properties like hardness and tensile bond strength. It was observed that after post coating heat treatment, crystallinity increased and porosity decreased which indicated recrystallization of amorphous phases of as-sprayed coatings. Heat treatment resulted into improvement in cross-sectional hardness, however sharp decrease in bond strength was observed.     

A methodology to assess the availability of next-generation data centers

The Journal of Supercomputing - Cloud data center providers benefit from software-defined infrastructure once it promotes flexibility, automation, and scalability. The new paradigm of...     

An Analytical Model for Identifying Suspected Users on Twitter

Abstract

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.