Abstract

The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed.

Highlights

  • Wireless Sensor Networks (WSNs) are validated as an integral part of the Internet of Things where they extend the Internet to the physical world [1,2]

  • In order to investigate the efficiency and performance of the suggested algorithm, we have developed a parallel Genetic Algorithm (GA) program based on MATLAB® Distributed Computing Server (MDCS) (The MathWorks, Inc., Natick, MA, USA) and Parallel Computing Toolbox (PCT) (The MathWorks, Inc., Natick, MA, USA)

  • Traditional reliability and performance optimization methods, such as the Markov model and state space analysis, have some defects such as being too time-consuming, facility for causing state space explosions and unsatisfactory assumptions of component execution independence, they are inapplicable to the ever-changing service composition applications (SCAs) in WSNs

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Summary

Introduction

Wireless Sensor Networks (WSNs) are validated as an integral part of the Internet of Things where they extend the Internet to the physical world [1,2]. SCAs in WSNs have some new characteristics that differ from those of traditional software systems, for example flexible evolution, continuous reaction and multi-target self-adaption These new characteristics are real challenges faced by researchers attempting to optimize the reliability and performance of SCAs in WSNs [10]. According to the business flows specification of user’s service requests as well as some business rules, the ASs corresponding to some of these new SNs may be selected to combine into the SCA during runtime by using the late binding mechanism in services composition technology [13,14]. Different from the optimization methods for reliability and performance used for the traditional software, ones used for the SCAs in WSNs pay more attention to the flexible measure, deduce and adoption mechanism of reliability and performance based on summative evaluation on the operation information in an open running environment [16,17].

Reliability and Performance Model for SCAs in WSNs
Reliability and Performance Definitions for SCAs in WSNs
Probability Distribution of Performance Rates for Any Component Service
Structure Function of Performance Rates for SCAs in WSNs
Advantage of UGF Technique
Reliability and Performance Definitions of SCAs in WSNs Based on UGF
Composite Operators of Reliability and Performance Indices Based on UGF
Architecture of WSN Service Systems with FT
FT Model in WSNs Service System
Determining the Number of SNs that Can Be Simultaneously Executed
Determining the Termination Time of SN
Evaluating the Execution Time Distribution of Clusters
Evaluating the Execution Time Distribution of the Entire System
Evaluating the Different Clusters Consecutively Executed on the Same Hardware
Optimizing the Structure of SCAs in WSNs
Experiments and Analysis
Experimental Environment
Experimental Analysis
Findings
Conclusions
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