In this paper, based on a large scale survey in Europe and China as well as corresponding laboratory studies, the influencing factors on the sound preference evaluation, considering social, demographical, physical, behavioural and psychological facets, have been systematically examined. Various sound types have been considered, including natural, human, mechanical and instrumental sounds. In terms of social/demographical factors, the results suggest that age and education level are two factors which generally influence the sound preference significantly, although the influence may vary with different types of urban open spaces and sounds. With increasing age or education level, people tend to prefer natural sounds and are more annoyed by mechanical sounds. It has also been found that gender, occupation and residence status generally would not influence the sound preference evaluation significantly, although gender has a rather strong influence for certain sound types such as bird sounds. In terms of physical factors (season, time of day), behavioural factors (frequency of coming to the site, reason for coming to the site), and psychological factors (site preference), generally speaking, their influence on the sound preference evaluation is insignificant, except for limited case study sites and certain sound types. The influence of home sound environment, in terms of sounds heard at home, on the sound preference has been found to be generally insignificant, except for certain sounds. It is noted that there are some correlations between social/demographical factors and the studied physical/behavioural/psychological factors, which should be taken into account when considering the influence of individual factors on sound preference . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 AbstractIn this paper, based on a large scale survey in Europe and China as well as corresponding laboratory studies, the influencing factors on the sound preference evaluation, considering social, demographical, physical, behavioural and psychological facets, have been systematically examined based on statistical analyses for each of the nineteen case study sites. Various sound types have been considered, including natural, human, mechanical and instrumental sounds. In terms of social/demographical factors, the results suggest that age and education level are two factors which universally influence the sound preference significantly, although the influence may vary with different types of urban open spaces and sounds. With increasing age or education level, people tend to prefer natural sounds and are more annoyed by mechanical sounds in general. It has also been found that gender, occupation and residence status generally would not influence the sound preference evaluation significantly, although gender has a rather strong influen...

This research aims to explore the feasibility of using computer-based models to predict the soundscape quality evaluation of potential users in urban open spaces at the design stage. With the data from large scale field surveys in 19 urban open spaces across Europe and China, the importance of various physical, behavioral, social, demographical, and psychological factors for the soundscape evaluation has been statistically analyzed. Artificial neural network (ANN) models have then been explored at three levels. It has been shown that for both subjective sound level and acoustic comfort evaluation, a general model for all the case study sites is less feasible due to the complex physical and social environments in urban open spaces; models based on individual case study sites perform well but the application range is limited; and specific models for certain types of location/function would be reliable and practical. The performance of acoustic comfort models is considerably better than that of sound level models. Based on the ANN models, soundscape quality maps can be produced and this has been demonstrated with an example.

The aim of this study is to analyze the effects of social, demographical and behavioral factors as well as long-term sound experience on the subjective evaluation of sound level in urban open public spaces. This is based on a series of large scale surveys in 19 urban open spaces in Europe and China. The results suggest that the effects of social/demographical factors, including age, gender, occupation, education and residential status, on the sound level evaluation are generally insignificant, although occupation and education are two related factors and both correlate to the sound level evaluation more than other factors. The effects of some behavioral factors, including wearing earphones, reading/writing and moving activities, are also insignificant on the sound level evaluation, but the watching behavior is highly related to the sound level evaluation. Compared to the social, demographical and behavioral factors, the long-term sound experience, i.e. the acoustic environment at home, significantly affect the sound level evaluation in urban open spaces. It is important to note that between the social/demographical factors, there are generally significant correlations, although the correlation coefficients may not be high. It is also noted that there are considerable variations between different urban open spaces.

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In sensor networks, the adversaries can inject false data reports from compromising nodes. Previous approaches for filtering false reports, notably statistical en-route filtering, adopt a simple strategy for grouping sensor nodes that requires redundant groups and thus decrease the filtering effectiveness. Worse still, they either suffer a threshold problem, which may lead to complete breakdown of the security protection when the threshold is exceeded, or are dependent on sink stationarity and specific routing protocols, which cannot work with mobile sinks and various routing protocols. In response to these, this paper proposes a scheme, referred to as Grouping-based Resilient Statistical En-route Filtering (GRSEF), in which nodes are grouped once deployed without requiring redundant groups and a location-aware approach based on terrain division along multiple axes is proposed for key derivation. The design of GRSEF, which is independent of sink stationarity and routing protocols, provides a well suitable en-routing filtering solution for sensor networks with mobile sinks. Analytical and simulation results verify that the scheme significantly improves the filtering effectiveness and efficiently achieves the resiliency against node compromise.

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Routing in vehicular network is a challenging task due to the characteristic of intermittent connectivity, especially when nodes behave selfishly in the real world. Previous works usually assume that all nodes in the network are willing to forward packets for others, which is impractical in real world. Selfish behaviors of nodes would degrade network performance greatly. In this paper, we propose SCR, a social contribution-based routing protocol, for selfish vehicular network. When making forwarding decision, SCR considers both the delivery probability to the destination and the social contributions of the relay node. The delivery probability is determined by the social relations among nodes and social contribution is used as the incentive to stimulate selfish nodes to be more cooperative, which consists of reciprocal contribution and community contribution. The node with higher delivery probability and lower social contributions is the preferred candidate for the next hop. Simulation results show that SCR achieves better performance than other social routing protocols with the incentive scheme.

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