An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications

The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique.     

SEAT-DSR: Spatial and energy aware trusted dynamic distance source routing algorithm for secure data communications in wireless sensor networks

This paper propose a novel secure routing mechanism called Spatial and Energy Aware Trusted Dynamic Distance Source Routing (SEAT-DSR) algorithm for enhancing the network life time of wireless sensor networks. Here, the spatial information, energy level, and the effectiveness of data quality are equalized by the Quality of Service (QoS) based energy aware routing algorithms. In addition to this approach, a standard clustering algorithm is also incorporates for grouping the wireless sensor nodes based on the trust score, spatial information, energy level and the distance between the nodes. In this SEAT-DSR is also capable of making decision over the evaluation metrics that are decided and expressed the QoS. Moreover, a new hierarchical trust mechanism is also introduced in this model which adopts multi-attributes of many wireless sensor nodes according to the data communication speed, data size, energy consumption, and the recommendation. This new hierarchical trust method relies over an improved the sliding window time by considering the presence of various attacks frequency to identify the attackers by discovering their anomalous behaviour. The proposed SEAT-DSR is evaluated by conducting many experiments in a simulation environment that creates by using Network Simulator-2 (NS2). The experimental results of the proposed algorithm are proved that the average packet transfer rate is increased drastically than the existing secure routing methodologies.     

Segment routing based energy aware routing for software defined data center

Despite the fact that most of the data centers are software-defined, the multifaceted network architecture and increase in network traffic make data centers to suffer from overhead. Multipath TCP supports multiple paths for a single routing session and ensures proper utilization of bandwidth over all available links. As rise in number of nodes in data center is frequent and drastic, scalability issue limits the performance of many existing techniques. Segment Routing is vibrant in reducing scalability disputes and routing overhead. Segment routing approach combined with MPTCP traffic result in efficient routing approach. The downfall of the link capacity due to drastic incoming traffic remains as a major concern in data center network which enforces preventing link energy depletion due to high network traffic. Our proposed work, segment routing based energy aware routing approach for software defined data center aims to achieve throughput maximization through preserving link residual capacity and proper utilization of links. As well, our approach shows a decrease in length of segment label stack with respect to maximum segment label depth. Analysis is done by comparing the executions of other existing approaches in a single-controller environment with our energy-aware routing approach in a distributed environment. Distributed controller setup prevents network from single point of failure. It helps to prevent controller overhead and provides improved network performance through throughput.     

Identifying influential segments from word co-occurrence networks using AHP

Identifying important segments in textual data seems to be an important area of research for various applications including topic modelling, trend detection, summarization and event detection. In existing research work, different metrics have been studied to analyse the word co-occurrence network. This research work contributes towards non-semantic and an unsupervised topic identification using the word co-occurrence networks. In this research work, keyphrase have been identified by preserving the lexical sequence using a directed and weighted word co-occurrence network. Further AHP (Analytic Hierarchy Process) model based upon four significant attributes of the word co-occurrence networks have been proposed to rank the keyphrases. Most frequently occurring segment is identified as an influential segment. Experimental results proved high effectiveness of the proposed approach. Results for the First Story Detection, 72 Twitter TDT, synthesized Rio Olympics dataset have been discussed to demonstrate its potential in precisely discovering influential segments.     

Energy management solutions in the Internet of Things applications: Technical analysis and new research directions

By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented.     

A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.     

Short-term meditation induces changes in brain resting EEG theta networks

Many studies have reported meditation training has beneficial effects on brain structure and function. However, very little is known about meditation-induced changes in brain complex networks. We used network analysis of electroencephalography theta activity data at rest before and after 1-week of integrative body–mind training (IBMT) and relaxation training. The results demonstrated the IBMT group (but not the relaxation group) exhibited significantly smaller average path length and larger clustering coefficient of the entire network and two midline electrode nodes (Fz and Pz) after training, indicating enhanced capacity of local specialization and global information integration in the brain. The findings provide the evidence for meditation-induced network plasticity and suggest that IBMT might be helpful for alterations in brain networks.     

Logical Modes of Attack in Argumentation Networks

This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in the metalevel to suit the needs of the application. The network-based system improves upon some of the attempts in the literature to define attacks in terms of defeasible proofs, the so-called rule-based systems. We also provide a number of examples and consider a rigorous case study, which indicate that our system does not suffer from anomalies. We define consequence relations based on a notion of defeat, consider rationality postulates, and prove that one such consequence relation is consistent.     

The utility of connectionism for motor learning: a reinterpretation of contextual interference in movement schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.     

The Utility of Connectionism for Motor Learning: A Reinterpretation of Contextual Interference in Movement Schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.     

The large-scale structure of semantic networks: statistical analyses and a model of semantic growth

We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.     

The Dynamics of Visual Word Recognition

This article provides an overview of a dynamical systems approach to visual word recognition. In this approach, the dynamics of word recognition are characterized in terms of a connectionist network model. According to this model, seeing a word results in changes in the pattern of activation over the nodes in the lexical network such that, over time, the network moves into an attractor state representing the orthographic, phonological, and semantic properties of that word. At a slower timescale, a learning process modifies the strengths of the connections among the nodes in a way that attunes the network to the statistical regularities in its environment. This view of word identification accommodates a wide body of empirical results, a representative sampling of which is discussed here. Finally, the article closes with a discussion of some of the theoretical issues that should be addressed as the dynamical approach continues to develop.     

Attention network test — The impact of social information on executive control,alerting and orienting

According to the attention network approach, attention is best understood in terms of three functionally and neuroanatomically distinct networks — alerting, orienting, and executive attention. An important question is whether social information influences the efficiency of these networks. Using the same structure as the Attentional Network Test (ANT), we developed a variant of this test to examine attentional effects in response to stimuli with and without social-cognitive content. Fish, drawings or photographs of faces looking to the left or right were used as target stimuli. Results collected from twenty-four university students showed that photographs of faces positively affected attentional orienting and executive control, whereas reduced the efficiency of alerting, as compared to both face drawings and fish. These results support the status of human faces as a special class of visual stimuli for the human attentional systems.     

Verbal fluency output in children aged 7-16 as a function of the production criterion: qualitative analysis of clustering, switching processes, and semantic network exploitation

Developmental changes in children's verbal fluency were explored in this study. One hundred and forty children aged from 7 to 16 completed four verbal fluency tasks, each with a different the production criterion (letter, sound, semantic, and free). The age differences were analyzed both in terms of number of words produced, and clustering, switching, and semantic network exploration. Analysis of the number of words produced showed a larger difference between the 7-8- and the 9-10-year-olds in semantic than in letter fluency, but this difference gradually disappeared with increasing age for semantic fluency while remaining constant for letter fluency. In letter fluency production, age modified both the number of switches and clusters formed whereas in semantic fluency tasks, only cluster size changed with age. Concerning the semantic network exploration indicators derived from the supermarket fluency task, the number of categories sampled increased from 11 to 12 years, but efficient semantic exploitation occurred only after the age of 13-14 years. These results are discussed in terms of the development of strategic retrieval components and categorical knowledge.     

The primate working memory networks

Working memory has long been associated with the prefrontal cortex, since damage to this brain area can critically impair the ability to maintain and update mnemonic information. Anatomical and physiological evidence suggests, however, that the prefrontal cortex is part of a broader network of interconnected brain areas involved in working memory. These include the parietal and temporal association areas of the cerebral cortex, cingulate and limbic areas, and subcortical structures such as the mediodorsal thalamus and the basal ganglia. Neurophysiological studies in primates confirm the involvement of areas beyond the frontal lobe and illustrate that working memory involves parallel, distributed neuronal networks. In this article, we review the current understanding of the anatomical organization of networks mediating working memory and the neural correlates of memory manifested in each of their nodes. The neural mechanisms of memory maintenance and the integrative role of the prefrontal cortex are also discussed.     

A parallel distributed processing model of stimulus-stimulus and stimulus-response compatibility.

A parallel distributed processing (PDP) model is proposed to account for choice reaction time (RT) performance in diverse cognitive and perceptual tasks such as the Stroop task, the Simon task, the Eriksen flanker task, and the stimulus-response compatibility task that are interrelated in terms of stimulus-stimulus and stimulus-response overlap (Kornblum, 1992). In multilayered (input-intermediate-output) networks, neuron-like nodes that represent stimulus and response features are grouped into mutually inhibitory modules that represent stimulus and response dimensions. The stimulus-stimulus overlap is implemented by a convergence of two input modules onto a common intermediate module, and the stimulus-response overlap by direct pathways representing automatic priming of outputs. Mean RTs are simulated in various simple tasks and, furthermore, predictions are generated for complex tasks based on performance in simpler tasks. The match between simulated and experimental results lends strong support for our PDP model of compatibility.     

Communication and Structured Correlation

Philosophers and social scientists have recently turned to Lewis sender–receiver games to provide an account of how lexical terms can acquire meaning through an evolutionary process. However, the evolution of meaning is contingent on both the particular sender–receiver game played and the choice of evolutionary dynamic. In this paper I explore some differences between models that presume an infinitely large and randomly mixed population and models in which a finite number of agents communicate with their neighbors in a social network. My results show that communication with neighbors is more conducive to the evolution of meaning than communication with strangers. Additionally, I show that the behavior of the system is highly dependent on the topological structure of the social network. I argue that a specific class of networks—small world graphs—is especially conducive to the evolution of meaning. This is because small world graphs have a short characteristic path length while still maintaining a high degree of correlation between neighbors. Since many actual social networks, such as friendship networks and nervous systems, are conjectured to be small world structures, these results indicate that these networks are quite hospitable to the efficient evolution of meaning.     

Affective information processing and the assessment of anxiety

A conceptualization of anxiety as comprising three loosely coupled response systems of overt behavior, verbal report, and physiological activation has proven useful in clinical and theoretical work. With this framework as a starting point, an information-processing approach to the study of emotion is described. Emotions are conceived as affective programs within the brain, with information coded as propositions organized into associative networks. Affective expression results when such a network is accessed and processed, which can occur when a sufficient number of propositions are activated by environmental stimuli and/or internal associations. It is hypothesized that information about the expressive physiology is an integral component of the associative structure, and that processing of the network accordingly results in measurable psychophysiological response. Data from studies of emotional imagery, as well as other areas of research, are reviewed in support of these theories. The utility of this approach for the assessment of anxiety disorders is discussed, and results of clinical studies are presented to suggest that individual differences in accessing and processing emotional information may bear significant implications for prognosis and treatment selection. It is speculated that differences among the anxiety disorders could be interpreted in terms of the degree of cognitive organization of the network, leading to potential refinement of current diagnostic categories. In conclusion, cognitive psychology paradigms are discussed in terms of their application to the assessment and treatment of anxiety disorders.The authors' research is supported in part by NIMH Grant MH 37757. Portions of this paper were presented at the NIMH Conference on Anxiety, Tuxedo, NY, September 1983. Several of its themes were developed there at more length, and this material may be found in the publication of the conference proceedings (Tuma & Maser, 1985).     

Outliers and Influential Observations in Exponential Random Graph Models

We discuss measuring and detecting influential observations and outliers in the context of exponential family random graph (ERG) models for social networks. We focus on the level of the nodes of the network and consider those nodes whose removal would result in changes to the model as extreme or “central” with respect to the structural features that “matter”. We construe removal in terms of two case-deletion strategies: the tie-variables of an actor are assumed to be unobserved, or the node is removed resulting in the induced subgraph. We define the difference in inferred model resulting from case deletion from the perspective of information theory and difference in estimates, in both the natural and mean-value parameterisation, representing varying degrees of approximation. We arrive at several measures of influence and propose the use of two that do not require refitting of the model and lend themselves to routine application in the ERGM fitting procedure. MCMC p values are obtained for testing how extreme each node is with respect to the network structure. The influence measures are applied to two well-known data sets to illustrate the information they provide. From a network perspective, the proposed statistics offer an indication of which actors are most distinctive in the network structure, in terms of not abiding by the structural norms present across other actors.