8 research outputs found
Tight Bounds for Black Hole Search with Scattered Agents in Synchronous Rings
We study the problem of locating a particularly dangerous node, the so-called
black hole in a synchronous anonymous ring network with mobile agents. A black
hole is a harmful stationary process residing in a node of the network and
destroying destroys all mobile agents visiting that node without leaving any
trace. We consider the more challenging scenario when the agents are identical
and initially scattered within the network. Moreover, we solve the problem with
agents that have constant-sized memory and carry a constant number of identical
tokens, which can be placed at nodes of the network. In contrast, the only
known solutions for the case of scattered agents searching for a black hole,
use stronger models where the agents have non-constant memory, can write
messages in whiteboards located at nodes or are allowed to mark both the edges
and nodes of the network with tokens. This paper solves the problem for ring
networks containing a single black hole. We are interested in the minimum
resources (number of agents and tokens) necessary for locating all links
incident to the black hole. We present deterministic algorithms for ring
topologies and provide matching lower and upper bounds for the number of agents
and the number of tokens required for deterministic solutions to the black hole
search problem, in oriented or unoriented rings, using movable or unmovable
tokens
Black Hole Search with Finite Automata Scattered in a Synchronous Torus
We consider the problem of locating a black hole in synchronous anonymous
networks using finite state agents. A black hole is a harmful node in the
network that destroys any agent visiting that node without leaving any trace.
The objective is to locate the black hole without destroying too many agents.
This is difficult to achieve when the agents are initially scattered in the
network and are unaware of the location of each other. Previous studies for
black hole search used more powerful models where the agents had non-constant
memory, were labelled with distinct identifiers and could either write messages
on the nodes of the network or mark the edges of the network. In contrast, we
solve the problem using a small team of finite-state agents each carrying a
constant number of identical tokens that could be placed on the nodes of the
network. Thus, all resources used in our algorithms are independent of the
network size. We restrict our attention to oriented torus networks and first
show that no finite team of finite state agents can solve the problem in such
networks, when the tokens are not movable. In case the agents are equipped with
movable tokens, we determine lower bounds on the number of agents and tokens
required for solving the problem in torus networks of arbitrary size. Further,
we present a deterministic solution to the black hole search problem for
oriented torus networks, using the minimum number of agents and tokens
Faulty node repair and dynamically spawned black hole search
New threats to networks are constantly arising. This justifies protecting network assets and mitigating the risk associated with attacks. In a distributed environment, researchers aim, in particular, at eliminating faulty network entities. More specifically, much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. However, the prevalence of faulty nodes requires an algorithm able to (a) identify faulty nodes that can be repaired without human intervention and (b) locate black holes, which are taken to be faulty nodes whose repair does require human intervention. In this paper, we consider a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as a virus v that can infect a previously repaired faulty node and turn it into a black hole. We refer to the task of repairing multiple faulty nodes and pointing out the location of the black hole as the Faulty Node Repair and Dynamically Spawned Black Hole Search. Wefirst analyze the attack model we put forth. We then explain (a) how to identify whether a node is either (1) a normal node or (2) a repairable faulty node or (3) the black hole that has been infected by virus v during the search/repair process and, (b) how to perform the correct relevant actions. These two steps constitute a complex task, which, we explain, significantly differs from the traditional Black Hole Search. We continue by proposing an algorithm to solve this problem in an
Searching for black holes in subways.
Abstract Current mobile agent algorithms for mapping faults in computer networks assume that the network is static. However, for large classes of highly dynamic networks (e.g., wireless mobile ad hoc networks, sensor networks, vehicular networks), the topology changes as a function of time. These networks, called delay-tolerant, challenged, opportunistic, etc., have never been investigated with regard to locating faults. We consider a subclass of these networks modelled on an urban subway system. We examine the problem of creating a map of such a subway. More precisely, we study the problem of a team of asynchronous computational entities (the mapping agents) determining the location of black holes in a highly dynamic graph, whose edges are defined by the asynchronous movements of mobile entities (the subway carriers). We determine necessary conditions for the problem to be solvable. We then present and analyze a solution protocol; we show that our algorithm solves the fault mapping problem in subway networks with the minimum number of agents possible, k = γ + 1, where γ is the number of carrier stops at black holes. The number of carrier moves between stations required by the algorithm in the worst case is , where n C is the number of subway trains, and l R is the length of the subway route with the most stops. We establish lower bounds showing that this bound is tight. Thus, our protocol is both agent-optimal and move-optimal
Locating and repairing faults in a network with mobile agents
International audienc