16 research outputs found
Coordination of Mobile Mules via Facility Location Strategies
In this paper, we study the problem of wireless sensor network (WSN)
maintenance using mobile entities called mules. The mules are deployed in the
area of the WSN in such a way that would minimize the time it takes them to
reach a failed sensor and fix it. The mules must constantly optimize their
collective deployment to account for occupied mules. The objective is to define
the optimal deployment and task allocation strategy for the mules, so that the
sensors' downtime and the mules' traveling distance are minimized. Our
solutions are inspired by research in the field of computational geometry and
the design of our algorithms is based on state of the art approximation
algorithms for the classical problem of facility location. Our empirical
results demonstrate how cooperation enhances the team's performance, and
indicate that a combination of k-Median based deployment with closest-available
task allocation provides the best results in terms of minimizing the sensors'
downtime but is inefficient in terms of the mules' travel distance. A
k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc
DISPATCH: An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals
This work presents an optimally-competitive algorithm for the problem of
maximum weighted online perfect bipartite matching with i.i.d. arrivals. In
this problem, we are given a known set of workers, a distribution over job
types, and non-negative utility weights for each pair of worker and job types.
At each time step, a job is drawn i.i.d. from the distribution over job types.
Upon arrival, the job must be irrevocably assigned to a worker and cannot be
dropped. The goal is to maximize the expected sum of utilities after all jobs
are assigned.
We introduce DISPATCH, a 0.5-competitive, randomized algorithm. We also prove
that 0.5-competitive is the best possible. DISPATCH first selects a "preferred
worker" and assigns the job to this worker if it is available. The preferred
worker is determined based on an optimal solution to a fractional
transportation problem. If the preferred worker is not available, DISPATCH
randomly selects a worker from the available workers. We show that DISPATCH
maintains a uniform distribution over the workers even when the distribution
over the job types is non-uniform