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Cognitive LF-Ant: a novel protocol for healthcare wireless sensor networks.

Sousa M, Lopes W, Madeiro F, Alencar M - Sensors (Basel) (2012)

Bottom Line: The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them.Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage.Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing.

View Article: PubMed Central - PubMed

Affiliation: Institute for Advanced Studies in Communications (Iecom), Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil. marcelo.portela@ee.ufcg.edu.br

ABSTRACT
In this paper, the authors present the Cognitive LF-Ant protocol for emergency reporting in healthcare wireless sensor networks. The protocol is inspired by the natural behaviour of ants and a cognitive component provides the capabilities to dynamically allocate resources, in accordance with the emergency degree of each patient. The intra-cluster emergency reporting is inspired by the different capabilities of leg-manipulated ants. The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them. Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage. Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing.

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The eta defuzzification after computing the input variables, local_distance and CH_dispersion, over nine fuzzy rules.
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f1-sensors-12-10463: The eta defuzzification after computing the input variables, local_distance and CH_dispersion, over nine fuzzy rules.

Mentions: Fuzzy logic decisions are based on IF-THEN rules, which are used to determine the value of output variables using approximate reasoning [31]. Furthermore, a fuzzy neural network can be used to acquire fuzzy rules based on the learning ability of neural networks and solve the lack of adaptability for possible changes in the reasoning environment [32]. The inference system is composed by linguistic variables and logical operators. The fuzzy rules used in the proposed protocol, to generate the value of the heuristic information, are presented in Table 1. Once the values of local_distance and CH_dispersion become small (Close) and great (Far), respectively, the fuzzy heuristic information presents the higher values, and thus, the greater chances to elect a cluster-head. The fuzzy variable eta is defuzzified (transformed to a crisp number) by the use of the Center of Area (CoA) method, which corresponds to calculating the centroid of the fuzzy output [33]. The membership functions adopted in this work are formed by triangles due the requirement of processing simplicity. The interaction between the fuzzy rules, over the values of local_distance = 0.179 (Close) and CH_dispersion = 0.868 (Far), is illustrated in Figure 1. The defuzzified value of 0.942 is equivalent to a Very High value of eta and to the point which splits in two equal parts the output area.


Cognitive LF-Ant: a novel protocol for healthcare wireless sensor networks.

Sousa M, Lopes W, Madeiro F, Alencar M - Sensors (Basel) (2012)

The eta defuzzification after computing the input variables, local_distance and CH_dispersion, over nine fuzzy rules.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472838&req=5

f1-sensors-12-10463: The eta defuzzification after computing the input variables, local_distance and CH_dispersion, over nine fuzzy rules.
Mentions: Fuzzy logic decisions are based on IF-THEN rules, which are used to determine the value of output variables using approximate reasoning [31]. Furthermore, a fuzzy neural network can be used to acquire fuzzy rules based on the learning ability of neural networks and solve the lack of adaptability for possible changes in the reasoning environment [32]. The inference system is composed by linguistic variables and logical operators. The fuzzy rules used in the proposed protocol, to generate the value of the heuristic information, are presented in Table 1. Once the values of local_distance and CH_dispersion become small (Close) and great (Far), respectively, the fuzzy heuristic information presents the higher values, and thus, the greater chances to elect a cluster-head. The fuzzy variable eta is defuzzified (transformed to a crisp number) by the use of the Center of Area (CoA) method, which corresponds to calculating the centroid of the fuzzy output [33]. The membership functions adopted in this work are formed by triangles due the requirement of processing simplicity. The interaction between the fuzzy rules, over the values of local_distance = 0.179 (Close) and CH_dispersion = 0.868 (Far), is illustrated in Figure 1. The defuzzified value of 0.942 is equivalent to a Very High value of eta and to the point which splits in two equal parts the output area.

Bottom Line: The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them.Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage.Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing.

View Article: PubMed Central - PubMed

Affiliation: Institute for Advanced Studies in Communications (Iecom), Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil. marcelo.portela@ee.ufcg.edu.br

ABSTRACT
In this paper, the authors present the Cognitive LF-Ant protocol for emergency reporting in healthcare wireless sensor networks. The protocol is inspired by the natural behaviour of ants and a cognitive component provides the capabilities to dynamically allocate resources, in accordance with the emergency degree of each patient. The intra-cluster emergency reporting is inspired by the different capabilities of leg-manipulated ants. The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them. Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage. Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing.

Show MeSH