c-INPRES: Coupling Analysis towards Locking Optimization in Ambient Intelligence

Zamudio, Víctor; Baltazar, Rosario; Casillas, Miguel Ángel; Callaghan, Vic

doi:10.1109/ie.2010.20

Cited by 4 publications

(3 citation statements)

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“…The scenarios of intelligent environments are governed by a set of rules, which are directly involved in the unstable behavior, and can lead the system to multiple changes over a period of time. These changes can cause interference with other devices, or unwanted behavior [ 1 – 4 ].…”

Section: Cyclic Instability In Intelligent Environmentsmentioning

confidence: 99%

“…Any computer system can have errors and Ambient Intelligence is not exempt from them. Cyclical instability is a fundamental problem characterized by the presence of unexpected oscillations caused by the interaction of the rules governing the agents involved [ 1 – 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Interaction Network is the digraph associated to the system and captures the dependencies of the rules between agents. INPRES finds the loops and locks a subset of agents on a loop, preventing them to change their state [ 1 – 4 ]. INPRES has been tested successfully in system with low density of interconnections and static rules (nomadic devices and time variant rules are not allowed).…”

Section: Introductionmentioning

confidence: 99%

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A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

Romero

Zamudio

Baltazar

et al. 2012

Sensors

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In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

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Section: Cyclic Instability In Intelligent Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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