Forthcoming articles

 


International Journal of Social and Humanistic Computing

 

These articles have been peer-reviewed and accepted for publication in IJSHC, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

 

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International Journal of Social and Humanistic Computing (5 papers in press)

 

Regular Issues

 

  • Will you accept my Job? A New Approach Towards Predicting Human Participation in Mobile Crowdsensing   Order a copy of this article
    by Tanveer Ahmed, Abhishek Srivasta 
    Abstract: In the last decade, the exponential growth of wireless devices have paved the way for several newand innovative mobile oriented paradigms. One such area that has started to receive attention in literature is mobile crowdsensing. Composed of ordinary people and their personal devices, the paradigm promises several astonishing developments that compel us to imagine a much smarter future. However, before we get to explore such a lucrative prospect, we need to focus our efforts on important issues that remain unaddressed. One specific aspect that has been repeatedly ignored in literature is the factor concerning human participation. This is because work has primarily focused on the technical capabilities of a mobile device, and has often neglected the issue of human participation. In this paper, therefore, we aim to address this shortcoming. We present a novel probabilistic approach to predict whether we can expect a response from the crowd or not. The proposed candidate selection algorithm takes its inspiration from statistics and handles the typical uncertainties in human behavior.Validation of the proposed framework is done in two ways. 1)We implement a prototype and deploy it over a cloud based Enterprise Service Bus, MuleESB. 2) We perform numerical testing on real datasets. With this experimental testbed, we will show the efficacy of the framework in realistic and actual deployment scenarios.
    Keywords: Mobile Computing; Human Participation Prediction; Mobile Crowdsensing.

  • Simulation analysis of the search effectiveness on information-based firefighting   Order a copy of this article
    by Lin Hui 
    Abstract: Nowadays, fire accident is still a thorny problem due to the current firefighting still heavily relies on the experience instead of information. Saving lives from fireground is the primary task in firefighting, in which the speed of effective search largely relies on the sufficient and instant information. When insufficient information situation follows firefighter tightly, the firefighters life can be jeopardized. Equipped firefighter with the advancing information technology, such as IR, laser range-finder, camera, augmented reality and an unmanned aerial vehicle for acquiring more fireground information may be useful for firefighting task. This study focuses on using Monte Carlo simulation model to quantify the feasible alternatives and finds out the significant effect by t-test. The result showed the time in search of victim is significantly reduced as using new way of firefighting. The contribution of this paper is to disclose the value of the proposed information-based technology in support of firefighting.
    Keywords: firefighting; simulation; obstacle; search; IoT.

  • Combined forecasting model of urban water consumption based on adaptive filtering and BP neural network   Order a copy of this article
    by Ban Fuchen, Wu Dan, Hei Yueming 
    Abstract: In order to solve the problem of improving the precision of urban short-term water consumption forecasting, the idea of combination forecasting is put forward. According to the water use data of a city, the time series prediction method and the explanatory prediction method are used to forecast the water use in the short term. In order to combine the advantages of the two forecasting methods, this paper proposes a combination forecasting method based on weight coefficient optimization theory. Compared with the single prediction model, the combined forecasting model has higher accuracy and stability.
    Keywords: Water Demand Prediction; Adaptive Filtering Method; BP Neural Network Method; Combined Forecasting Model.

  • Link Stable Routing with Minimal Delay Nodes for MANETS   Order a copy of this article
    by Jayalakshmi P, Saravanan R 
    Abstract: Multi-hop routing in MANETs requires good resource managing protocols. The efficient route from source to destination in the network is identified by determining the level of signal strength from intermediate hops. The link quality between the intermediate nodes is determined by calculating the signal strength level. The received signal strength is one method to estimate the medium characteristics and distance between the intermediate nodes which is one of the quality of service required by the network. The nodes with high signal strength and high bandwidth are identified as minimal delay nodes so as to determine the reliable path. The data is sent through minimal delay nodes in the shortest path from source to destination. The better link quality improves the performance metrics such as packet delivery ratio, throughput and simultaneously reduces the control message overheads, average and end-to-end delay compared to some of the existing routing protocols. The experimental study shows that the proposed routing protocol is 30% better in packet delivery ratio, throughput, reduced control message overheads, average and end-to-end delay than the existing Stable and Delay Constrained Routing protocol.
    Keywords: Routing; Minimal delay nodes; Signal strength; MANET.rnrn.

  • A Simultaneous Evolution for Analyzing the Interactions between CO2 emissions and national income   Order a copy of this article
    by Cheng-Min Chuang, Chih-Chuan Yeh 
    Abstract: In this paper, we estimate the causal interrelationships between growth and CO2 emissions within a simultaneous equations framework and obtain identification using a novel heteroskedasticity-based method. After the removal of the bias, we find that per capita GDP and CO2 emissions are strongly interrelated. Moreover, we split our dataset into high-income, middle-upper-income and low-income countries, and the results indicate that the beneficial effect of per capita GDP on CO2 emissions in high-income panel countries is greater than that in middle-upper panel countries. However, quicker economic development induces higher pollution in low-income countries. On the other hand, the effect of CO2 emissions results in the improvement of per capita GDP for the low to middle-upper income panel but a decrease in the improvement of per capita GDP for the high-income countries.
    Keywords: Economic growth; CO2 emissions; Simultaneous equations; Endogeneity bias.