Markovian agent modeling swarm intelligence algorithms in wireless sensor networks

Bruneo, Dario; Scarpa, Marco; Bobbio, A.; Cerotti, Davide; Gribaudo, Marco

doi:10.1016/j.peva.2010.11.007

Cited by 35 publications

(28 citation statements)

References 17 publications

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“…In a similar manner pheromone gradients have been adapted in the WSN literature as an abstract means of studying the evolution of routes from source to sink nodes. Several models have been built to investigate the spread of pheromone in such networks [Bruneo et al 2012;Guenther et al 2013]. We show how to capture those models in PALOMA.…”

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems

Feng

Hillston

Galpin

2016

ACM Trans. Model. Comput. Simul.

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Spatially distributed collective adaptive systems are an important class of systems, which pose significant challenges to modelling due to the size and complexity of their state spaces. This problem is acute when the dynamic behaviour of the system must be captured, for example in order to predict system performance. In this paper we present an abstraction technique which automatically derives a moment-closure approximation of the dynamic behaviour of a spatially distributed collective adaptive system from a discrete representation of the entities involved. The moment-closure technique is demonstrated to give accurate estimates of dynamic behaviour, although the number of ordinary differential equations generated for the second order joint moments can grow large in some cases. For these cases, we propose a rigorous model reduction technique and demonstrate its use to substantially reduce the computational effort with only limited impact on the accuracy if the reduction threshold is set appropriately. All the techniques reported in this paper are implemented in a tool which is freely available for download.

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Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems

Feng

Hillston

Galpin

2016

ACM Trans. Model. Comput. Simul.

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“…As a result, accurate models to estimate a system's performance are largely missing. Some exceptions model the Collection Tree Protocol (CTP) [6] to improve its performance in industrial scenarios [12], analyze swarm intelligence algorithms for sensor networks based on the Markovian agent model [10], apply diffusion approximation techniques to estimate the end-to-end packet travel times assuming opportunistic packet forwarding rules [29], or model generic multi-hop functionality through population continuoustime Markov chains [8]. Nevertheless, the validation of these models is limited to numerical simulations, which lack precisely those real-world dynamics of low-power wireless links that make accurate protocol modeling so complex and difficult.…”

Section: B Link-based Transmissionsmentioning

confidence: 99%

“…As a result, existing models often stop at the link layer, achieving model errors in the range of 2-7% [9]. Only a few attempts exist to model also higher-layer functionality [8], [10]- [12]. However, their validation is limited to numerical simulations, lacking precisely those real-world dynamics that complicate the modeling.…”

Section: Introductionmentioning

confidence: 99%

On Modeling Low-Power Wireless Protocols Based on Synchronous Packet Transmissions

Zimmerling

Ferrari

Mottola

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pairwise link-based transmissions (LT). ST improve the one-hop packet reliability to an extent that efficient multi-hop protocols with little network state are feasible.This paper studies whether ST also enable simple yet accurate modeling of these protocols. Our contribution to this end is twofold. First, we show, through experiments on a 139-node testbed, that characterizing packet receptions and losses as a sequence of independent and identically distributed (i.i.d.) Bernoulli trials-a common assumption in protocol modeling but often illegitimate for LT-is largely valid for ST. We then show how this finding simplifies the modeling of a recent ST-based protocol, by deriving (i) sufficient conditions for probabilistic guarantees on the end-toend packet reliability, and (ii) a Markovian model to estimate the long-term energy consumption. Validation using testbed experiments confirms that our simple models are also highly accurate; for example, the model error in energy against real measurements is 0.25%, a figure never reported before in the related literature.

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“…Several case studies [2][3][4] demonstrated that this is a powerful and useful framework. However the model specification is in terms of matrices which capture the possible interactions and influences between agents.…”

Section: Introductionmentioning

confidence: 99%

PALOMA: A Process Algebra for Located Markovian Agents

Feng

Hillston

2014

Lecture Notes in Computer Science

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Abstract. We present a novel stochastic process algebra that allows the expression of models representing systems comprised of populations of agents distributed over space, where the relative positions of agents influence their interaction. This language, PALOMA, is given both discrete and continuous semantics and it captures multi-class, multi-message Markovian agent models (M 2 MAM). Here we present the definition of the language and both forms of semantics, and demonstrate the use of the language to model a flu epidemic under various quarantine regimes.

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Markovian agent modeling swarm intelligence algorithms in wireless sensor networks

Cited by 35 publications

References 17 publications

Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems

Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems

On Modeling Low-Power Wireless Protocols Based on Synchronous Packet Transmissions

PALOMA: A Process Algebra for Located Markovian Agents

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