Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks

Purpose - Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper. Design/methodology/approach - In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed. Findings - For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error. Originality/value - This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN

Route prioritisation in a multi-agent transportation environment via multi-attribute decision making

Effective management of the vehicles plays an important role in transportation sector. There are some novel approaches in software engineering which can be used to dynamically control the trucks. One of the example technologies is multi-agent systems. Multi-agent-based technologies could be used for truck dispatching in real time. The trucks in such systems have their own transportation plan and they can decide about their routes throughout their operations. Any truck agent must select a route to pickup or deliver a transportation order while doing its transportation operation. However, truck driver route preference is also crucial for the transportation performances because of morale, motivation and psychological conditions of drivers have a considerable effect on transportation operation success. In this paper, driver route preference model is incorporated into a multi-agent-based vehicle dispatching system which uses fuzzy graph theoretical-matrix permanent approach which is a novel method in multi-attribute decision making (MADM). The model is also compared with fuzzy TOPSIS method. Spearman rank correlation test is used to assess the correlation between the ranks. © 2016 Inderscience Enterprises Ltd