Information diffusion by local communication of multiple mobile robots

Arai, Tamio; Yoshida, Eiichi; Ota, Jun

doi:10.1109/icsmc.1993.390769

Cited by 31 publications

(18 citation statements)

References 5 publications

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“…The simulation results were noticeably different from the ones that other researchers (see, for example, [1] and [3]) have attained with the epidemic information diffusion model with a pure random mobility pattern of the network nodes. While the old results share the common S-shaped form, this new model has an exponential growth behavior: the growth rate reaches its maximum right after the spreading information is released at the exchange pipe, i.e.…”

Section: A Information Diffusion Functioncontrasting

confidence: 74%

“…Information diffusion in ad hoc networks has been studied by, for example, Arai et al [1] and Khelil et al [3], and in both of these studies some free random mobility model is used. Arai et al have also included stationary information sources in their study.…”

Section: A Research With An Epidemic Diffusion Modelmentioning

confidence: 99%

“…where n i (t), i∈ [1,3], is a function of diffusion level within the above mentioned three factor groups, P i is the number of nodes within the group i, and k i is a constant which depends on the parameters of the simulation.…”

Section: A Information Diffusion Functionmentioning

confidence: 99%

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MP2P network as an information diffusion channel

Kurhinen¹,

Vuori²

VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005.

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Section: A Information Diffusion Functioncontrasting

confidence: 74%

Section: A Research With An Epidemic Diffusion Modelmentioning

confidence: 99%

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MP2P network as an information diffusion channel

Kurhinen¹,

Vuori²

VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005.

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“…We reuse the symbol ∆t to represent the length of each interval but here we set ∆t = 16l 2 2 . Our local bound is built within each subcube (and pair of neighboring subcubes) in the time increment ∆t: 3 be a region that can be covered by a ball of radius 2l 2 under the L ∞ -norm. Let A f and A u be subsets of infected and uninfected agents in W at time t such that |A f | = m 1 , |A u | = m 2 , and max{m 1 , m 2 } =l 2 / log 2 n. Given any initial placement of the agents of A f and A u , let M (t) be the number of agents in A u that become infected at time t + ∆t.…”

Section: Upper Boundmentioning

confidence: 99%

Information Dissemination via Random Walks in d-Dimensional Space

Lam¹,

Liu²,

Mitzenmacher³

et al. 2012

Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms

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We study a natural information dissemination problem for multiple mobile agents in a bounded Euclidean space. Agents are placed uniformly at random in the d-dimensional space {−n, ..., n} d at time zero, and one of the agents holds a piece of information to be disseminated. All the agents then perform independent random walks over the space, and the information is transmitted from one agent to another if the two agents are sufficiently close. We wish to bound the total time before all agents receive the information (with high probability). Our work extends Pettarin et al's work [13], which solved the problem for d ≤ 2. We present tight bounds up to polylogarithmic factors for the case d = 3. (While our results extend to higher dimensions, for space and readability considerations we provide only the case d = 3 here.) Our results show the behavior when d ≥ 3 is qualitatively different from the case d ≤ 2.In particular, as the ratio between the volume of the space and the number of agents varies, we show an interesting phase transition for three dimensions that does not occur in one or two dimensions. 0 1 each with probability 1/(2d). If an agent is at a boundary, so there is no edge in one or more directions, we treat each missing edge as a self-loop. Let Ξ 1 (t), ..., Ξ m (t) ∈ {0, 1} each be a random variable, where Ξ i (t) represents whether the agent a i is infected at time step t. We assume Ξ 1 (0) = 1 and Ξ i (0) = 0 for all i = 1. The value Ξ i (t) will change from 0 to 1 if at time t it is within distance 1 to another infected agent a j . (We use distance 1 instead of distance 0 to avoid parity issues.) Once a value Ξ j (t) becomes 1, it stays 1. Definition 1.1. (Information diffusion problem). Let A 1 , A 2 , . . . , A m ∈ V d be the initial positions of the agents a 1 , . . . , a m and let S 1 t (A 1 ), S 2 t (A 2 ), . . . , S m t (A m ) be m independent random walks starting at A 1 , . . . , A m respectively, so that S i t (P ) is the position of agent a i at time t given that at t = 0 its position was P ∈ V d . The infectious state of each agent at time step t is a binary random variable Ξ i (t) such that • Ξ 1 (0) = 1, Ξ i (0) = 0 for all other i, and

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“…Internet Protocols), have proven highly successful as a platform for experiments in collective robotics [4], [5]. Arai et al [8] proposed an information diffusion mechanism for distributing command data to multiple robots and analysed the overall time-delays incurred. Genovese et al [7] considered both swarm-like self-organisation and communication.…”

mentioning

confidence: 99%

Distributed Sensing and Data Collection Via Broken Ad Hoc Wireless Connected Networks of Mobile Robots

Winfield

2000

Distributed Autonomous Robotic Systems 4

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Intelligent Autonomous Systems (engineering) laboratory, University of the West of England Bristol, Coldharbour Lane, Frenchay, Bristol BS16 lQY, UK.Abstract. This paper reports on ongoing work to develop ad hoc wireless networking for application in distributed mobile robotics. The paper discusses a mission scenario in which a number of mobile robots are required to autonomously disperse into a physically bounded region, take sensor readings at predetermined time intervals, and then communicate the sense data back to a single collection point. There is assumed to be no overall command and control structure. The robots are assumed to be equipped with low-power short-range wireless network interfaces, which only allow direct communication with near neighbours, and overall an ad hoc (multi-hop) wireless networking scheme is employed. Furthermore, it is assumed that there are insufficient robots to allow full wireless connectivity through the region so that, at any instant, the robots actually appear as a set of disconnected sub-nets (i.e. a broken ad hoc network). The paper proposes a mechanism that exploits the mobility of the robots to overcome the lack of overall wireless network connectivity, resulting in a sub-optimal but robust scheme for garnering sense data. The paper presents simulation results that show how the arithmetic mean data collection delay varies with the number of robots deployed. The paper concludes that in mission scenarios that do not require data collection in real-time and where occasional data loss (erasure) is acceptable, then the proposed data collection mechanism is feasible, robust and economical in terms of the number of robots required.

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Information diffusion by local communication of multiple mobile robots

Cited by 31 publications

References 5 publications

MP2P network as an information diffusion channel

MP2P network as an information diffusion channel

Information Dissemination via Random Walks in d-Dimensional Space

Distributed Sensing and Data Collection Via Broken Ad Hoc Wireless Connected Networks of Mobile Robots

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