Searching for a Black Hole in Synchronous Tree Networks

Czyzowicz, Jurek; Kowalski, Dariusz R.; Μάρκου, Ευριπίδης; Pełc, Andrzej

doi:10.1017/s0963548306008133

Cited by 45 publications

(30 citation statements)

References 10 publications

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“…Most of the existing work deals with the black hole search problem for a single black hole [9,10,15,16,17,18,19,20,21,22,23,27,32,33,34]. Some work has been done for multiple black holes in [8,35], although both of these papers use the synchronous model, as do some of the single black hole papers [9,10,33,34]. Existing solutions can be classified based on the network model: synchronous and asynchronous.…”

Section: Black Hole Search -A Classification Reviewmentioning

confidence: 99%

“…In [10], Czyzowicz looked at the same problem in trees, and gave optimal black hole search algorithms for two extreme classes of trees: the class of lines and the class of trees in which any internal node (including the root which is the starting node) has at least two children. In [34], Klasing et al consider the same problem assuming that the map of the network is given.…”

Section: A Synchronous Modelmentioning

confidence: 99%

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A Review of Computation Solutions by Mobile Agents in an Unsafe Environment

Zarrad¹,

Daadaa²

2013

IJACSA

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Abstract-Exploration in an unsafe environment is one of the major problems that can be seen as a basic block for many distributed mobile protocols. In such environment we consider that either the nodes (hosts) or the agents can pose some danger to the network. Two cases are considered. In the first case, the dangerous node is a called black hole, a node where incoming agents are trapped, and the problem is for the agents to locate it. In the second case, the dangerous agent is a virus; an agent moving between nodes infecting them, and the problem is for the "good" agents to capture it, and decontaminate the network.In this paper, we present several solutions for a black-hole and network decontamination problems. Then, we analyze their efficiency. Efficiency is evaluated based on the complexity, and the effort is in the minimization of the number of simultaneous decontaminating elements active in the system while performing the decontamination techniques.

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Section: Black Hole Search -A Classification Reviewmentioning

confidence: 99%

Section: A Synchronous Modelmentioning

confidence: 99%

A Review of Computation Solutions by Mobile Agents in an Unsafe Environment

Zarrad¹,

Daadaa²

2013

IJACSA

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“…This black hole search problem has been originally studied in ring networks [12] and has been extensively investigated in various settings since then (e.g., see [4,6,8,10,13,14,19,24]). In order to locate the black hole, some of the agents of the team will necessarily have to enter the dangerous site.…”

Section: Related Workmentioning

confidence: 99%

“…Tight bounds on the number of moves have been established for some classes of trees [6]. In the case of general networks finding the optimal strategy is shown to be NP-hard [5,20] and approximation algorithms are given in [20,21].…”

Section: Related Workmentioning

confidence: 99%

Time Optimal Algorithms for Black Hole Search in Rings

Balamohan

Flocchini

Miri

et al. 2011

Discrete Math. Algorithm. Appl.

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Abstract. In a network environments supporting mobile entities (called robots or agents), a black hole is harmful site that destroys any incoming entity without leaving any visible trace. The black-hole search problem is the task of a team of k > 1 mobile entities, starting from the same safe location and executing the same algorithm, to determine within finite time the location of the black hole. In this paper we consider the black hole search problem in asynchronous ring networks of n nodes, and focus on the time complexity. It is known that any algorithm for black-hole search in a ring requires at least 2(n − 2) time in the worst case. The best algorithm achieves this bound with a team of n − 1 agents with an average time cost 2(n − 2), equal to the worst case. In this paper we first show how the same number of agents using 2 extra time units from optimal in the worst case, can solve the problem in only 7 4 n − O(1) time on the average. We then prove that the optimal average case complexity 3 2 n − O(1) can be achieved without increasing the worst case using 2(n − 1) agents Finally we design an algorithm that achieves asymptotically optimal both worst case and average case time complexity employing an optimal team of k = 2 agents, thus improving on the earlier results that required O(n) agents.

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“…The computability and complexity of BHS depend on a variety of factors, first and foremost on whether the system is asynchronous [5,6,7,8,9] or synchronous [2,4,3,13]. Indeed the nature of the problem changes drastically and dramatically.…”

Section: Introductionmentioning

confidence: 99%