In this paper we consider planning problems in relationalMarkov processes where objects may “appear” or “disap-pear”, perhaps depending on previous actions or propertiesof other objects. For instance, problems which require to ex-plicitly generate or discover objects fall into this category. Inour formulation this requires to explicitly represent the un-certainty over the number of objects (dimensions or factors)in a dynamic Bayesian networks (DBN). Many formalisms(also existing ones) are conceivable to formulate such prob-lems. We aim at a formulation that facilitates inference andplanning. Based on a specific formulation we investigate twoinference methods—rejection sampling and reversible-jumpMCMC—to compute a posterior over the process conditionedon the first and last time slice (start and goal state). We willdiscuss properties, efficiency, and appropriateness of eachone.

The main hindrance to a widespread market penetration of battery-powered electric vehicles (BEVs) has been their limited energy reservoir resulting in cruising ranges of few hundred kilometers unless one allows for recharging or switching of depleted batteries during a trip. Unfortunately, recharging typically takes several hours and battery switch stations providing fully recharged batteries are still quite rare – certainly not as widespread as ordinary gas stations. For not getting stranded with an empty battery, going on a BEV trip requires some planning ahead taking into account energy characteristics of the BEV as well as available battery switch stations. In this paper we consider very basic, yet fundamental problems for E-Mobility: Can I get from A to B and back with my BEV without recharging in between? Can I get from A to B when allowed to recharge? How can I minimize the number of battery switches when going from A to B? We provide efficient and mathematically sound algorithms for these problems that allow for the energy-aware planning of trips.

Public sensing is a new research area in the fields of wireless sensor networks and mobile computing. It leverages the mobile sensors and system resources readily available in mobile phones to execute sensing tasks. In order to plan, execute and adapt large-scale sensing tasks, applications need to query for the available resources, e.g. the density of certain sensors. We investigate how such information can be provided, and we propose a resource manager for public sensing. Our primary goal is to minimize the energy consumed by the mobile devices to make public sensing feasible without disturbing users. We propose a cluster-based protocol for collecting local views of the resource state using local ad-hoc communication since this is much more energy-efficient than long-range (e.g. cellular) communication. We compare our solution to a standard approach where mobile devices communicate their resource states using the cellular phone network. We show that 65% of the energy is saved and the communication load on the infrastructure is reduced by 90% while an average delivery ratio of 93% is retained.

We consider the problem of routing electric vehicles (EV) in the most energy-efficient way within a road network taking into account both their limited energy supply as well as their ability to recuperate energy. Employing a classical result by Johnson and an observation about Dijkstra under non-constant edge costs we obtain O(n log n +m) query time after a O(nm) preprocessing phase for any road network graph whose edge costs represent energy consumption or recuperation.If the energy recuperation is height induced in a very natural way,the preprocessing phase can even be omitted. We then adapt a technique for speeding-up (unconstrained) shortest path queries to our scenario to achieve a speed-up of another factor of around 20. Our results drastically improve upon the recent results in (Artmeier et al. 2010) and allow for route planning of EVs in an instant even on large networks.