Academic literature on the topic 'Recharge en route'

Sensor networks are composed of miniaturized wireless sensor nodes with limited capacity and energy source. Generally, these sensor networks are used, in many applications, to monitor inaccessible environments (battlefields, volcano monitoring, animal tracking…), hence the impossibility to replace or to recharge the batteries. As sensors may be deployed in a large area, radio transceivers are the most energy consuming of sensor nodes, which means that their usage needs to be very efficient in order to maximize node life, which leads us to maximize the network's life. In wireless sensor networks and in order to transmit its data, a node can route its messages towards destination, generally the base station, either by using small or large hops, so optimizing the hop length can extend significantly the lifetime of the network. This chapter provides a simple way to verify, which makes the energy consumption minimal by choosing proper hop length.

As discussed in the previous chapter, the term problem is used in a general sense to refer to any task that requires resolution. These tasks may range from solving a mathematical problem to formulating a business strategy, from generating an engineering prototype to conceiving an artistic design. A problem is called easy if the identification of an acceptable solution is straightforward. The label of easiness refers to the generation of the solution rather than its implementation. According to this view, finding the average value of a thousand numbers is as easy as calculating the mean of two values, since the procedure is equally straightforward. In contrast, a hard or difficult problem is one whose resolution is not readily discernable. A common source of difficulty lies in the fact that the ultimate objective is not known a priori. This situation is reminiscent of the fictional detective rummaging through a ransacked house. “What are you looking for?” asks his companion. “I don't know—but I'll know it when I find it!” In a more sedate context, the same situation applies to an investigator who wants to develop a science of manufacturing but cannot specify beforehand the nature of such a discipline. Manufacturing is one arena which until recently was regarded as a domain so complex that it would remain only an art rather than a science. A second and perhaps more prevalent difficulty in resolving a problem relates to the route rather than the destination: the desired objective may be known, but not its means of attainment. This situation occurs when an automotive engineer must design an electric car that can travel over 1000 kilometers between battery recharges. It also occurs when a federal committee must develop a policy to contain the outbreak of a new epidemic: it is not clear to what extent emphasis should be placed on public education, medical research, governmental regulation, or other mechanisms for prevention and redress. The resolution of such difficult problems requires a creative approach. In fact, we can summarize the preceding discussion in the following definitions.

The problem considered in this paper is to produce both routes and refuelling schedules for a heterogeneous fleet of autonomous mobile vehicles that provide efficient group work performance in large-scale persistent monitoring missions. In the model, the route for the group of vehicles must be determined on the extended time period to ensure regular and well-timed inspections of a set of tasks at scheduled intervals. The routes are limited by the speed limitation, onboard equipment and fuel capacity of the vehicles alongside with visitation frequency and equipment requirements of each task. Fu