J.C. Cuevas-Martinez

Knowledge-based duty cycle estimation in wireless sensor networks: Application for sound pressure monitoring

Wireless sensor networks comprise an important research area and a near future for industry and communications. Wireless sensor networks contain resource-constrained sensor nodes that are powered by small batteries, limited process and memory and wireless communication. These features give sensors their versatility and drawbacks, such as their limited operating lifetimes. To feasibly deploy wireless sensor networks with isolated motes, several approaches and solutions have been developed; the most common, apart from using alternative power sources such as solar panels, are those that put sensors to sleep for time periods established by the application. We thus propose a fuzzy rule-based system that estimates the next duty cycle, taking the magnitude being tested and battery charge as input. To show how it works, we compare an analytical delta system to our contribution. As an application to test both systems, a sound pressure monitoring application is presented. The results have shown that the fuzzy rule-based system better predicts the evolution of the magnitude by which errors committed by idle periods decrease. This work also shows that application-oriented duty cycle control can be an alternative for measuring systems, thus saving battery and improving sensor node lifetime, with a reasonable loss of precision.

Wireless Intelligent Sensors Management Application Protocol-WISMAP.

Although many recent studies have focused on the development of new applications for wireless sensor networks, less attention has been paid to knowledge-based sensor nodes. The objective of this work is the development in a real network of a new distributed system in which every sensor node can execute a set of applications, such as fuzzy ruled-base systems, measures, and actions. The sensor software is based on a multi-agent structure that is composed of three components: management, application control, and communication agents; a service interface, which provides applications the abstraction of sensor hardware and other components; and an application layer protocol. The results show the effectiveness of the communication protocol and that the proposed system is suitable for a wide range of applications. As real world applications, this work presents an example of a fuzzy rule-based system and a noise pollution monitoring application that obtains a fuzzy noise indicator.

Improving hybrid ad hoc networks

Intelligent selection of a gateway in ad hoc networks based in performance metrics.Selection method based on a fuzzy system optimized by a genetic algorithm.The fitness function of the genetic algorithm is designed with another fuzzy system in order to accomplish it optimization task.This system over-performs other similar systems in the Internet gateway election problem. The selection of an appropriate and stable route that enables suitable load balancing of Internet gateways is an important issue in hybrid mobile ad hoc networks. The variables employed to perform routing must ensure that no harm is caused that might degrade other network performance metrics such as delay and packet loss. Moreover, the effect of such routing must remain affordable, such as low losses or extra signaling messages. This paper proposes a new method, Steady Load Balancing Gateway Election, based on a fuzzy logic system to achieve this objective. The fuzzy system infers a new routing metric named cost that considers several networks performance variables to select the best gateway. To solve the problem of defining the fuzzy sets, they are optimized by a genetic algorithm whose fitness function also employs fuzzy logic and is designed with four network performance metrics. The promising results confirm that ad hoc networks are characterized by great uncertainty, so that the use of Computational Intelligence methods such as fuzzy logic or genetic algorithms is highly recommended.

Propagation of Agent Performance Parameters in Wireless Sensor Networks

Wireless sensor networks are composed of resource-constrained sensor nodes, powered by batteries, with limited CPU and memory, and wireless communication. In spite of the fact that sensor nodes are resource-constrained devices, several soft computing technologies have been adapted to them. In order to save battery, sensor nodes work in cycles based on awake and sleep modes. In this work we propose a method, based on a differential decision system, to calculate dynamic parameters that control the awake-sleep cycle in a multi-agent sensor structure and their propagation to other sensor nodes in a network. As an application of the proposed system, a sound pressure monitoring application is presented. Results have shown that the proposed method utilizes less work cycles than continuous measuring systems, saving battery and improving the lifetime of sensor nodes, with a reasonable lost of precision.

Fast implementation of an improved parametric audio coder based on a mixed dictionary

This paper deals with the application of adaptive signal models for representing transients and sinusoids at the same stage in a parametric audio coder. To accomplish such a goal, we search for sparse approximations by means of matching pursuit with a mixed dictionary, instead of using two different dictionaries that operate in cascade. In such sense, complex exponentials and wavelet packets are chosen for modeling the tonal and transient features of an audio signal, respectively. At each iteration of the pursuit, the mixed dictionary function that extracts the most energy from the residue is selected. This function will be either a complex exponential or a wavelet packet, depending on the characteristics of the residue at that iteration. Experimental results clearly show the objective (compression rate) and subjective (% preference) advantages of the mixed dictionary over two cascaded dictionaries. The approach proposed in this paper is successfully applied for parametric audio coding purposes, assuring better perceptual audio quality than MPEG2/4-AAC at 16 Kbits/s for most of the CD-quality one channel audio signals considered for testing.

Cluster Head Enhanced Election Type-2 Fuzzy Algorithm for Wireless Sensor Networks

This approach presents a fully distributed clustering solution for wireless sensor networks. It relies on the results of an interval type-2 fuzzy logic system that gives each node the chance to be a cluster head. Taking into account the limited computational resources of the sensors, this inference system has been carefully adapted to be run in each node through a sampling process of the entire solution space of the fuzzy system. The input variables of the system are obtained from the information that each node derives from its performance metrics and those related to its neighbors. The acquisition of these last data does not incur in any additional control packets. The results obtained show a significant improvement in the network lifetime when compared with other recent approaches. This improvement takes place even when contrasting with centralized methods.

Adaptive signal models for wide-band speech and audio compression

This paper deals with the application of adaptive signal models for parametric speech and audio compression. The matching pursuit algorithm is used for extracting sinusoidal components and transients in audio signals. The resulting residue is perceptually modelled as a noise like signal. When a transient is detected, psychoacoustic-adapted matching pursuits are accomplished using a wavelet-based dictionary followed of an harmonic one. Otherwise, matching pursuit is applied only to the harmonic dictionary. This multi-part model (Sines + Transients + Noise) is successfully applied for speech and audio coding purposes, assuring high perceptual quality at low bit rates (close to 16 kbps for most of the signals considered for testing).

Architecture for redundant knowledge reduction in embedded fuzzy rule-based systems into wireless sensor network nodes

Nowadays, growing interest exists in the integration of artificial intelligence technologies into Wireless Sensor Networks. However, little attention has been paid to integrate Fuzzy Rule-Based Systems in these networks. The objective of this work is to optimize the design of embedded Fuzzy Rule-Based Systems by means of redundant knowledge reduction considering the lack of accuracy. Results have shown that a right redundant knowledge reduction produces a great increase in time of battery discharge and a remarkable decrease in battery consumption.