Secure Platform Over Wireless Sensor Networks

Life sciences: general issue

Prediction of Slow-Moving Landslide Mobility Due to Rainfall Using a Two-Wedges Model

In the present study, the landslides cyclically reactivated by water-table oscillations due to rainfall are dealt with. The principal kind of motion that usually characterizes such landslides is a slide with rather small velocity. As another feature, soil deformations are substantially accumulated inside a narrow shear zone situated below the landslide body so that the latter approximately slides rigidly. Within this framework, a new approach is developed in this paper to predict the mobility of this type of landslides due to rainfall. To this end, a two-wedges model is used to schematize the moving soil mass. Some analytical solutions are derived to link rain recordings with water-table fluctuations and in turn to landslide displacements. A well-documented landslide frequently activated by rainfall is studied to check the forecasting capacity of the proposed method

Using drones for parcels delivery process

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Kinematics of the Maierato Landslide (Calabria, Southern Italy)

Abstract On 15 February 2010, a landslide of great dimensions occurred at Maierato (Southern Italy) after a long rainy period. Although the zone was continuously affected by movements, no monitoring system was installed before the landslide. However, many photos were taken to document the occurrence of deformations and two videos were filmed during the paroxysmal phase of the event. Photos and videos are used in the present study to reconstruct the kinematics of the landslide. A geotechnical model of the slope is also defined on the basis of the results from field and laboratory tests

The resource constrained shortest path problem with uncertain data: a robust formulation and optimal solution approach

International audienceThe Resource Constrained Shortest Path Problem (RCSP P) models several applications in the fields of transportation and communications. The classical problem supposes that the resource consumptions and the costs are certain and looks for the cheapest feasible path. These parameters are however hardly known with precision in real applications, so that the deterministic solution is likely to be infeasible or suboptimal. We address this issue by considering a robust counterpart of the RCSP P. We focus here on resource variation and model its variability through the uncertainty set defined by Bertismas and Sim (2003,2004), which can model the risk aversion of the decision maker through a budget of uncertainty. We solve the resulting problem to optimality through the well-known three phase approach dealing with bounds computation, network reduction and gap closing. In particular, we compute robust bounds on the resource consumption and cost by solving the robust shortest path problem and the dual robust Lagrangian relaxation, respectively. Dynamic programming is used to close the duality gap. Upper and lower bounds are used to reduce the dimension of the network and incorporated in the dynamic programming in order to fathom unpromising states. An extensive computational phase is carried out in order to asses the behavior of the defined strategy comparing its performance with the state-of-the-art. The results highlight the effectiveness of our approach in solving to optimality * 1 benchmark instances for RCSP P when Γ is not too large, tailored for the robust counterpart. For larger values of Γ, we show that the most efficient method combines deterministic preprocesing with the iterative algorithm from Bertsimas and Sim (2003). We also illustrate the failure probability of the robust solutions through Monte Carlo sampling

Crowd-shipping: a new efficient and eco-friendly delivery strategy

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Crowd-shipping with time windows and transshipment nodes

Crowd-shipping is a delivery policy in which, in addition to standard vehicle routing practices, ordinary people accept to deviate from their route to deliver items to other people, for a small compensation. In this paper we consider a variant of the problem by taking into account the presence of intermediate depots in the service network. The occasional drivers can decide to serve some customers by picking up the parcels either from the central depot or from an intermediate one. The objective is to minimize the total cost, that is, the conventional vehicle cost, plus the occasional drivers’ compensation. We formulate the problem and present a variable neighborhood search heuristic. To analyze the benefit of the crowd-shipping transportation system with intermediate depots and to assess the performance of our heuristic, we consider small- and large-size instances generated from the Solomon benchmarks. A computational analysis is carried out with the aim of gaining insights into the behavior of both conventional vehicles and occasional drivers, and of analyzing the performance of our methodology in terms of effectiveness and efficiency. Our computational results show that the proposed heuristic is highly effective and can solve large-size instances within short computational times.</p

Energy Efficient Mobile Target Tracking Using Flying Drones

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