The conventional data-based routing protocols are usually vulnerable to a large number of energy voids or hotspots in Wireless Sensor Networks (WSNs). In order to address this problem, we propose Mobile Intelligent Fog Computing: An Energy-efficient Cross-layer-sensing Clustering Method (ECCM). The first, according to the cross-layer projection principle, the proposed algorithm employs the sensingevent-driven mechanism to project the fog nodes onto the sensing layer, and constructs a powerful virtual control node. Then the control procedure of the cluster-based routing protocol in sensor networks is uploaded to the fog layer and the fog computation is employed to achieve the distributed clustering of the event-field nodes. The second, the optimized data aggregation routing is constructed, which centers the projectile fog nodes. The data in the bottom-layer routing of the sensor network is thus replaced, and the network load is balanced and reduced. The third, in the optimization of the routing protocol, we introduce the Particles Swarm Optimization, (PSO) algorithm and elect a group of optimal nodes as the cluster heads, without the cost of any competition overhead, the energy overhead of the network can be effectively reduced and balanced, which curbs the rapid exhaustion of the node energy and prolongs the network lifetime. Finally, it is shown by the simulation results that the construction and the maintenance of the routing structure are small, which could optimize the data aggregation efficiency and improve the network performance.INDEX TERMS Fog computation, wireless sensor network, clustering method, routing protocol, particles swarm optimization.
Background: Whether patients with panic disorder behave differently or not when recognizing the facial expressions of emotion remains unsettled. Sampling and Methods: We tested 21 outpatients with panic disorder and 34 healthy subjects, with a photo set from the Matsumoto and Ekman Japanese and Caucasian facial expressions of emotion, which includes anger, contempt, disgust, fear, happiness, sadness, and surprise. Results: Compared to the healthy subjects, patients showed lower accuracies when recognizing disgust and fear, but a higher accuracy when recognizing surprise. Conclusions: These results suggest that the altered specificity to these emotions leads tso self-awareness mechanisms to prevent further emotional reactions in panic disorder patients.
Theories of fuzzy sets and rough sets have emerged as two major mathematical approaches for managing uncertainty that arises from inexact, noisy, or incomplete information. They are generalizations of classical set theory for modelling vagueness and uncertainty. Some integrations of them are expected to develop a model of uncertainty stronger than either. The present work may be considered as an attempt in this line, where we would like to study fuzziness in probabilistic rough set model, to portray probabilistic rough sets by fuzzy sets. First, we show how the concept of variable precision lower and upper approximation of a probabilistic rough set can be generalized from the vantage point of the cuts and strong cuts of a fuzzy set which is determined by the rough membership function. As a result, the characters of the (strong) cut of fuzzy set can be used conveniently to describe the feature of variable precision rough set. Moreover we give a measure of fuzziness, fuzzy entropy, induced by roughness in a probabilistic rough set and make some characterizations of this measure. For three well-known entropy functions, including the Shannon function, we show that the finer the information granulation is, the less the fuzziness (fuzzy entropy) in a rough set is. The superiority of fuzzy entropy to Pawlak's accuracy measure is illustrated with examples. Finally, the fuzzy entropy of a rough classification is defined by the fuzzy entropy of corresponding rough sets. and it is shown that one possible application of it is lies in measuring the inconsistency in a decision table.
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