Priority based data reporting algorithm in wireless sensor networks

A cluster-based organization with two phases to separate priority calculation and network activity operations is developed. In the election-state phase, clusters are organized by those nodes with sufficient residual energy level (REL) that are distributed as evenly as possible in the network. For each network round, each node decides its role (such as, cluster header (CH) or a normal node) and its priority level for activity operation. The priority level also affects the active/sleep scheduling in the node. In the steady-state phase, the positive priority levels of active nodes determine their activity order to avoid transmission collision, and to reduce redundant data transmission and sensing coverage. Since the data reporting to the sink is usually performed by a CH delivery chain, subsidiary CHs are selected to assist with data delivery and to share the loading of CHs. The formulae for computing priority levels are analyzed and verified by the examples with valid parameters. The experimental results show that the two phases efficiently consume node energy and achieve energy savings.     

A simple method to solve the network congestion for spitial architcture

Scheduling algorithm always plays an important role in the spatial architecture for the contradiction between the finite network bandwidth and the abundant execution resources. This article provides a simple method to solve the contention for network resource in one of the spatial architecture, i.e. the tera-op, reliable, intelligently adaptive processing system (TRIPS) processor. The method improves the performance of network by increasing the bypass bandwidth which can transmit the data in the internal of every execution unit, and converting the proportion of remote communication by the deep scheduling algorithm. The deeply optimized algorithm is realized to verify the validity of the method, and the performance increase 9% for floating point spec2000 benchmark is got.     

Theoretical analysis of Er<Superscript>3+</Superscript>-doped telluride glass fiber amplifier for 2.700 μm laser amplification

The rate equations and power evolution equations of erbium-doped telluride glass fiber amplifier for both 1.530 and 2.700 μm lasers are solved numerically, the dependences of gain spectra on fiber length, dopant concentration and pump power are analyzed, and the gain of 2.700 μm laser is calculated and compared with the experimental result from reference. The numerical analysis shows that with 8 × 10²⁴ ion/m³ erbium ion concentration, 5m fiber length and 600mW pump power, the gains at 1.530 and 2.700 μm may achieve 23dB or so. With larger power pump and higher dopant concentration, a net gain of 17 dB is obtained from the Er³⁺-doped telluride glass fiber amplifier for 110mW input signal. This fiber amplifier is promising for both 1.530 μm signal amplification and 2.700 μm laser amplification.     

Substructuring technique for dynamics analysis of flexible beams with large deformation

In this paper, the substructuring technique is extended for the dynamics simulation of flexible beams with large deformation. The dynamics equation of a spatial straight beam undergoing large displacement and small deformation is deduced by using the Jourdain variation principle and the model synthesis method. The longitudinal shortening effect due to the transversal deformation is taken into consideration in the dynamics equation. In this way, the geometric stiffening effect, which is also called stress stiffening effect, is accounted for in the dynamics equation. The transfer equation of the flexible beam is obtained by assembling the dynamics equation and the kinematic relationship between the two connection points of the flexible beam. Treating a flexible beam with small deformation as a substructure, one can solve the dynamics of a flexible beam with large deformation by using the substructuring technique and the transfer matrix method. The dynamics simulation of a flexible beam with large deformation is carried out by using the proposed approach and the results are verified by comparing with those obtained from Abaqus software.     

A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.     

Job accessibility and employment outcomes: which income groups benefit the most?

Improving job accessibility can increase the probability for individual persons to be employed and reduce their commutes. Empirical research suggests that the relationship between job accessibility and employment outcomes differ across income groups, but no research has investigated the difference or explored which income groups benefit the most from job accessibility improvements. This research fills the gap by examining six income groups in the Los Angeles metropolitan area. Results show that job accessibility affects the employment status of medium-to-low income groups (household income between US$25,000 and US$75,000). For the lowest-income group (household income lower than US$25,000), owning a car significantly improves their chances to be employed, but job accessibility has no effect. On the other hand, higher job accessibility is associated with shorter commuting distance for the other five income groups, but not for the lowest-income group. These results suggest that transportation and land use policies need to address the specific needs of distinct population groups and underscore the importance of spatial access for the middle-class, which tends to be overlooked in the literature on transportation equity.     

A behavioral choice model of the use of car-sharing and ride-sourcing services

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014–2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.     

The role of socio-economic,built environment and psychological factors in parental mode choice for their children in an Iranian setting

This research investigated the role of parental psychological and socio-economic factors as well as built environment for the choice of their children’s (primary school pupils, aged 7–9 years) travel mode to school in Rasht, Iran. A total of 1078 questionnaires were distributed (return rate of 80 percent) among pupils in nine primary schools in January 2014. A mixed logit (ML) model was employed due to its ability to test heterogeneity among parents and also to determine its possible sources. Results of random coefficient ML modelling showed that several psychological, socio-economic and built environment characteristics were significant factors in parental mode choice. Only walking time perception to school had a significant random normal distribution coefficient and no other psychological and socio-economic variable had a random effect. Further investigation by random coefficient analysis showed that the possible source of household preference heterogeneity could be to own two or more cars. Regarding psychological variables, strong parental worry about their children walking alone to school had a negative impact on allowing them to walk to school. Parents who evaluated poor contextual and design preconditions for walking tended to choose school service more than private car and walking. Parents with stronger environmental personal norms were more willing to allow their children to walk. The findings suggest that infrastructural measures, such as sidewalk facilities, neighborhood security and safety, encourage parents to allow children to walk to school. Information campaigns targeting environmental norms may increase walking among pupils in an Iranian setting.