We address the problem of discovering routes in strongly connected planar geometric networks with directed links. Motivated by the necessity for establishing communication in wireless ad hoc networks in which the only information available to a vertex is its immediate neighborhood, we are considering routing algorithms that use the neighborhood information of a vertex for routing with constant memory only. We solve the problem for three types of directed planar geometric networks: Eulerian (in which every vertex has the same number of incoming and outgoing edges), Outerplanar (in which a single face contains all vertices of the network), and Strongly Face Connected, a new class of geometric networks that we define in the article, consisting of several faces, each face being a strongly connected outerplanar graph.
Persuasive technologies are increasingly ubiquitous, but the strategies they utilise largely originate in America. Consumer behaviour research shows us that certain persuasion strategies will be more effective on some cultures than others. We claim that the existing strategies will be less effective on non-American audiences than they are on American audiences, and we use information from interviews to show that there exists much scope to develop persuasive technologies from a collectivism-focused perspective. To illustrate the development of such a tool, we describe the design of a collectivism-focused financial planning tool.
Given a connected geometric graph G, we consider the problem of constructing a t-spanner of G having the minimum number of edges. We prove that for every t with 1 1+1/t) edges. This bound almost matches the known upper bound, which states that every connected weighted graph with n vertices contains a t-spanner with O(tn1+2/(t+1)) edges. We also prove that the problem of deciding whether a given geometric graph contains a t-spanner with at most K edges is NP-hard. Previously, this NP-hardness result was only known for non-geometric graphs.
The verification of non-functional requirements of software models (such as performance, reliability, scalability, security, etc.) requires the transformation of UML models into different analysis models such as Petri nets, queueing networks, formal logic, etc., which represent the system at a higher level of abstraction. The paper proposes a new "abstraction-raising" transformation approach for generating analysis models from UML models. In general, such transformations must bridge a large semantic gap between the source and the target model. The proposed approach is illustrated by a transformation from UML to Klaper (Kernel LAnguage for PErformance and Reliability analysis of component-based systems).
We consider the rendezvous problem for identical mobile agents (i.e., running the same deterministic algorithm) with tokens in a synchronous torus with a sense of direction and show that there is a striking computational difference between one and more tokens. More specifically, we show that 1) two agents with a constant number of unmovable tokens, or with one movable token, each cannot rendezvous if they have o(log n) memory, while they can perform rendezvous with detection as long as they have one unmovable token and O(log n) memory; in contrast, 2) when two agents have two movable tokens each then rendezvous (respectively, rendezvous with detection) is possible with constant memory in an arbitrary n × m (respectively, n × n) torus; and finally, 3) two agents with three movable tokens each and constant memory can perform rendezvous with detection in a n × m torus. This is the first publication in the literature that studies tradeoffs between the number of tokens, memory and knowledge the agents need in order to meet in such a network.
A black hole is a highly harmful host that disposes of visiting agents upon their arrival. It is known that it is possible for a team of mobile agents to locate a black hole in an asynchronous ring network if each node is equipped with a whiteboard of at least O(log n) dedicated bits of storage. In this paper, we consider the less powerful token model: each agent has has available a bounded number of tokens that can be carried, placed on a node or removed from it. All tokens are identical (i.e., indistinguishable) and no other form of communication or coordination is available to the agents. We first of all prove that a team of two agents is sufficient to locate the black hole in finite time even in this weaker coordination model. Furthermore, we prove that this can be accomplished using only O(nlogn) moves in total, which is optimal, the same as with whiteboards. Finally, we show that to achieve this result the agents need to use only O(1) tokens each.