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102 K. Kloch et al. large-scale systems in which intelligent mobile devices interact with each other and with humans. A specific application scenario within the project is the study how sensing, communication, and cooperation between mobile devices (e.g., sensor enabled smart phones) can be used to steer crowd behavior in emergency situations (e.g., to speed up evacuation or prevent panic). Ensuring that information can be effectively spread between the devices in an ad-hoc fashion (infrastructure, even including mobile phones, cannot be assumed to work in such situations, as the case of the London subway bombings has shown) is a key requirement. Another obvious application of our model is the spread of malware through peer-to-peer interactions between mobile devices. Second, on theoretical level, it extends previous approaches on information and virus spread (see [1,3,8,9] for related work) by specifically considering the effects of mobility and radio range. We provide an analytical solution for the ‘infection ratio’ (which is the key parameter in system description) and validate our results in extensive simulations. Third, in the area of organic computing, so far little attention has been given to the use of analytical complex system models as means of understanding and controlling the evolution of self-organizing IT systems (see [5] for some discussion). The system that we are investigating is easily mapped to the corresponding model, as information spread is obviously closely related to the spread of disease. Nonetheless, it is a good initial example of a design where global properties and phase transitions can be analytically derived from the rules for simple local interactions. We see it as a first step in the direction of using analytical models as means of achieving control in self-organized systems. 2 The Scenario This work is part of a large project to investigate large-scale, complex interactions between intelligent mobile devices and humans in their environment. In essence, it aims to apply models and concepts from complexity science and organic computing to ambient intelligence environments. Ambient intelligence research to date has concentrated on how individual intelligent devices interact with a single user or small groups of users. The questions that we ask include: How can such devices influence collective behavior? What sort of global effect can emerge from the coupled interactions and feedbacks between many different users and devices? How can we predict such global effects (and thus design systems in the sense of controlled emergence) from local interaction rules embedded in each individual device? A key issue underlying many of the research questions that we are addressing is the spread of information. Assuming that devices forward information to others in their physical proximity according to certain rules: How will information spread on a global scale? Can we, in the sense of controlled self-organization, use simple control parameters like user mobility and interaction range to ensure (or prevent) rapid spread?
Ad-Hoc Information Spread between Mobile Devices 103 The specific application that has motivated this work is disaster management. For example, as described in a London Assembly Meeting Report 1 , during the London subway bombings mobile phone communication broke down and lack of information has been a key problem for many of the people stuck inside the stations and trains. Thus, the ability to propagate information through the crowd in an ad-hoc fashion would have been of significant use. In more advanced scenarios that we are examining, phones are able to sense crowd motion and environmental conditions and propagate them throughout the crowd to optimize evacuation ([4]). Other applications include social interactions (e.g., ‘flash mobs’), media distribution and the spread of malware. 2.1 Model Assumptions For the simulation and modeling, some simplifying assumptions have been made. All mobile agents (representing, e.g., persons with cellular phones) are placed in a square board of a fixed size. To reduce the boundary effect of agents hitting the walls, the sides of the square are wrapped left-to-right and up-to-down (periodic boundary conditions), so that the actual simulation is being carried out on a torus. At the beginning, the agents are placed uniformly on the board and only a fraction of agents has some interesting information or malware to be spread (status: infected), while all others are initially uninfected. The direction of motion of each agent is chosen randomly. Each agent moves along a straight line for a random number of steps. The length of the straight paths varies randomly from a few steps to half the size of the board. After that, the direction of the agent and the length of its straight path is randomly changed, and the procedure is repeated. The achieved motion is random and – at the same time – resembles the movement of agents in a crowd. Each mobile agent carries a mobile device and is surrounded by the device’s radio range. The radio range is modeled by a flat disc. If an uninfected device collides with the radio range of an infected device, it will also get infected immediately and change its status to infected. With a certain probability, in each simulation step, an agent will reset its infected mobile device, and thus, will get rid of the infection without getting immune. The model parameters can be summarized as follows: (1) number of agents: N (since it is assumed that all agents carry only one device, N is equal to the number of devices), (2) initial infection ratio: I ∈ [0 ...1] (probability that a device will be infected initially), (3) system length: L unit lengths (overall area of L × L square unit lengths under consideration, kept constant in the presented scenario), (4) radio (interaction) range: R, (5) velocity of agents: v (in unit lengths per simulation step), and, optionally, (6) device reset probability: P ∈ [0 ...1] (probability that an agent will reset its infected mobile device within a simulation step). 1 see http://www.london.gov.uk/assembly/resilience/2005/77reviewdec01/ minutes/transcript.pdf
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Abdullah, Tariq 174 Alima, Luc Onan
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