Power-Aware Collective Tree Exploration

Dynia, Miroslaw; Korzeniowski, Marcin; Schindelhauer, Christian

doi:10.1007/11682127_24

Cited by 30 publications

(30 citation statements)

References 8 publications

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“…Assumptions similar to our paper have been made in [11,16,24] where the mobile agents are constrained to travel a fixed distance to explore an unknown graph, [11,16], or tree, [24]. In [11,16] a mobile agent has to return to its home base to refuel (or recharge its battery) so that the same maximal distance may repeatedly be traversed.…”

Section: Related Workmentioning

confidence: 96%

“…Consequently, in most tasks involving moving agents, like exploration, searching or pattern formation, the distance traveled is the main optimization criterion (cf. [2,3,11,12,15,16,22,24,26,33]). Single agent exploration of an unknown environment has been studied for graphs, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…In [11,16] a mobile agent has to return to its home base to refuel (or recharge its battery) so that the same maximal distance may repeatedly be traversed. [24] gives an 8-competitive distributed algorithm for a set of agents with the same amount of power exploring the tree starting at the same node.…”

Section: Related Workmentioning

confidence: 99%

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Collecting Information by Power-Aware Mobile Agents

Anaya

Chalopin

Czyzowicz

et al. 2012

Lecture Notes in Computer Science

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Abstract. A set of identical, mobile agents is deployed in a weighted network. Each agent possesses a battery -a power source allowing the agent to move along network edges. Agents use their batteries proportionally to the distance traveled. At the beginning, each agent has its initial information. Agents exchange the actually possessed information when they meet. The agents collaborate in order to perform an efficient convergecast, where the initial information of all agents must be eventually transmitted to some agent. The objective of this paper is to investigate what is the minimal value of power, initially available to all agents, so that convergecast may be achieved. We study the question in the centralized and the distributed settings. In the distributed setting every agent has to perform an algorithm being unaware of the network. We give a linear-time centralized algorithm solving the problem for line networks. We give a 2-competitive distributed algorithm achieving convergecast for tree networks. The competitive ratio of 2 is proved to be the best possible for this problem, even if we only consider line networks. We show that already for the case of tree networks the centralized problem is strongly NP-complete. We give a 2-approximation centralized algorithm for general graphs.

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Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

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Collecting Information by Power-Aware Mobile Agents

Anaya

Chalopin

Czyzowicz

et al. 2012

Lecture Notes in Computer Science

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“…An ex-ploration strategy for trees with global communication is given in [7], achieving a competitive ratio of (4 − 2/k) for the objective of minimizing the maximum number of edges traversed by an agent. In [5] a corresponding lower bound of 3/2 is provided.…”

Section: Related Workmentioning

confidence: 99%

Fast collaborative graph exploration

Dereniowski

Disser

Kosowski

et al. 2015

Information and Computation

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We study the following scenario of online graph exploration. A team of k agents is initially located at a distinguished vertex r of an undirected graph. At every time step, each agent can traverse an edge of the graph. All vertices have unique identifiers, and upon entering a vertex, an agent obtains the list of identifiers of all its neighbors. We ask how many time steps are required to complete exploration, i.e., to make sure that every vertex has been visited by some agent.We consider two communication models: one in which all agents have global knowledge of the state of the exploration, and one in which agents may only exchange information when simultaneously located at the same vertex. As our main result, we provide the first strategy which performs exploration of a graph with n vertices at a distance of at most D from r in time O(D), using a team of agents of polynomial size k = Dn 1+ < n 2+ , for any > 0. Our strategy works in the local communication model, without knowledge of global parameters such as n or D.We also obtain almost-tight bounds on the asymptotic relation between exploration time and team size, for large k.

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“…There are related problems discussed in [12][13] where an nnode tree is to be completely explored by a group of k mobile robots that are initially located at the root node. In [12], the minmax objective with respect to the distance travelled by the robot is considered for the cases where the tree is known a priori as well as when the previous knowledge of the tree does not exist.…”

mentioning

confidence: 99%

On the traversals of multiple mobile sinks in sensor networks

Nesamony¹,

Vairamuthu²,

Orłowska³

2007

2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications

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A class of applications involving wireless sensor networks requires more than one mobile sink to traverse through the sensor field to visit every sensor node in order to collect sensor data or to recharge the nodes or to calibrate the nodes. A natural problem arising in this scenario will be to accomplish the entire process in minimum time. Over some intrinsic assumptions, this problem is reduced to determining the paths of the mobile sinks where the maximum length path is minimised. A comprehensive exploration of problems of this class in the literature is considered. A heuristic approach is also provided for a specific instance of this problem where there are two mobile sinks. The performance of the heuristic is examined based on the experiments run and the paper concludes with some motivation for the extension of this work towards further research. Index Terms-k-TSP, minmax objective, mobile sink, sensor network1-4244-1094-0/07/$25.00

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Power-Aware Collective Tree Exploration

Cited by 30 publications

References 8 publications

Collecting Information by Power-Aware Mobile Agents

Collecting Information by Power-Aware Mobile Agents

Fast collaborative graph exploration

On the traversals of multiple mobile sinks in sensor networks

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