7,429 research outputs found
Collaborative Delivery with Energy-Constrained Mobile Robots
We consider the problem of collectively delivering some message from a
specified source to a designated target location in a graph, using multiple
mobile agents. Each agent has a limited energy which constrains the distance it
can move. Hence multiple agents need to collaborate to move the message, each
agent handing over the message to the next agent to carry it forward. Given the
positions of the agents in the graph and their respective budgets, the problem
of finding a feasible movement schedule for the agents can be challenging. We
consider two variants of the problem: in non-returning delivery, the agents can
stop anywhere; whereas in returning delivery, each agent needs to return to its
starting location, a variant which has not been studied before.
We first provide a polynomial-time algorithm for returning delivery on trees,
which is in contrast to the known (weak) NP-hardness of the non-returning
version. In addition, we give resource-augmented algorithms for returning
delivery in general graphs. Finally, we give tight lower bounds on the required
resource augmentation for both variants of the problem. In this sense, our
results close the gap left by previous research.Comment: 19 pages. An extended abstract of this paper was published at the
23rd International Colloquium on Structural Information and Communication
Complexity 2016, SIROCCO'1
Minimizing the Cost of Team Exploration
A group of mobile agents is given a task to explore an edge-weighted graph
, i.e., every vertex of has to be visited by at least one agent. There
is no centralized unit to coordinate their actions, but they can freely
communicate with each other. The goal is to construct a deterministic strategy
which allows agents to complete their task optimally. In this paper we are
interested in a cost-optimal strategy, where the cost is understood as the
total distance traversed by agents coupled with the cost of invoking them. Two
graph classes are analyzed, rings and trees, in the off-line and on-line
setting, i.e., when a structure of a graph is known and not known to agents in
advance. We present algorithms that compute the optimal solutions for a given
ring and tree of order , in time units. For rings in the on-line
setting, we give the -competitive algorithm and prove the lower bound of
for the competitive ratio for any on-line strategy. For every strategy
for trees in the on-line setting, we prove the competitive ratio to be no less
than , which can be achieved by the algorithm.Comment: 25 pages, 4 figures, 5 pseudo-code
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Broadcasting with Mobile Agents in Dynamic Networks
We study the standard communication problem of broadcast for mobile agents moving in a network. The agents move autonomously in the network and can communicate with other agents only when they meet at a node. In this model, broadcast is a communication primitive for information transfer from one agent, the source, to all other agents. Previous studies of this problem were restricted to static networks while, in this paper, we consider the problem in dynamic networks modelled as an evolving graph. The dynamicity of the graph is unknown to the agents; in each round an adversary selects which edges of the graph are available, and an agent can choose to traverse one of the available edges adjacent to its current location. The only restriction on the adversary is that the subgraph of available edges in each round must span all nodes; in other words the evolving graph is constantly connected. The agents have global visibility allowing them to see the location of other agents in the graph and move accordingly. Depending on the topology of the underlying graph, we determine how many agents are necessary and sufficient to solve the broadcast problem in dynamic networks. While two agents plus the source are sufficient for ring networks, much larger teams of agents are necessary for denser graphs such as grid graphs and hypercubes, and finally for complete graphs of n nodes at least n-2 agents plus the source are necessary and sufficient. We show lower bounds on the number of agents and provide some algorithms for solving broadcast using the minimum number of agents, for various topologies
A feedback-enhanced learning approach for routing in WSN
Much research in sensor networks focuses on optimizing traffic originating at multiple sources destined for a single, base station sink. Our work reverses this assumption, targeting scenarios where individual sensor data is sent to multiple destinations. In this case, the data path that produces the least network cost is unlikely to overlap completely with any of the optimal routes between the individual pairs of source/destination nodes. If the entire topology is known, an offline approach can likely find this minimum path. However this is an unrealistic assumption. Instead, our approach uses only local information and converges toward optimal. The novelty of our approach is a technique for actively exploring alternate data routes, sharing feedback regarding route fitness, and learning better routes. While non-optimal choices are made during the discovery phase, the resulting, learned path has lower cost than the initial path. Further, our protocol identifies multiple paths with equal cost, providing additional opportunities for saving energy by switching among alternate routes throughout the lifetime of the application. This paper describes our feedbackbased protocol, shows simulation results demonstrating its benefits and explores the future opportunities of the learning technique presented
- …